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 .
Status of Claims
This action is in reply to the claims filed on 01/28/2026.
Claims 1-2, 5, 7-9, 12, 14-16, and 19-20 are amended.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The IDS filed 01/16/2026 has been reviewed and considered.
Allowable Subject Matter
Claims 4, 7, 11, and 14 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101 set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Claim Rejections- 35 U.S.C. § 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.
Under Step 1 of the subject matter eligibility (SME) analysis described in MPEP 2106.03, the instant claims fall within the four statutory categories of invention identified by 35 U.S.C. 101. In the instant case, claims 1-7 are directed to a method, claims 8-14 are directed to a system, and claims 15-20 is directed to a manufacture. Claims 1, 8, and 15 are parallel in nature, therefore, the analysis will use claim 1 as the representative claim.
In Step 2A Prong One, it must be considered whether the claims recite a judicial exception. Claim 1, as exemplary, recites abstract concepts including: training ... for recommending one or more vehicle seats using a set of characteristics of previously recommended vehicle seats, wherein the set of characteristics of previously recommended vehicle seats includes a ratio of training data to validation; receiving ... a request for a vehicle seat recommendation; receiving ... input data; determining ... a set of characteristics of the input data; generating ... using the set of characteristics of the input data, a vehicle seat recommendation score for the one or more vehicle seats; ranking ... the one or more vehicle seats by vehicle seat recommendation score to generate a vehicle seat recommendation list, wherein the vehicle seat recommendation list includes all vehicle seats ranked above a threshold; and presenting ... the vehicle seat recommendation list to a client device.
These identified limitations recite the abstract idea of “recommending one or more vehicle seats”, which falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas as it relates to commercial interactions of sales activities or behaviors. Recommending products (vehicle seats) to a user directly facilitates or drives purchasing decisions, exemplifying a sales activity. Accordingly, claims 1, 8, and 15 recite an abstract idea. See MPEP 2106.04.
In Step 2A Prong Two, examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
Instant claims 1, 8, and 15 recite additional elements including: a computer; training, by one or more processors, a machine learning model; validating, by the one or more processors, a trained machine learning model, wherein the trained machine learning model is validated by a determination that the trained machine learning model satisfies one or more validation metrics applied to the validation data; generating, by a machine learning model; a client device; a computer system; one or more processors; a non-transitory program memory coupled to the one or more processors and storing executable instructions; and a tangible, non-transitory computer-readable medium storing executable instructions. The computer, client device, computer system, processors, and memory are each recited at a high-level of generality (i.e., as a generic device performing generic computer functions of transmitting, receiving, and storing information) such that it amounts to no more than “apply it” or mere instruction to implement the abstract idea on a computer. The recited machine-learning techniques are high-level steps including “training” using features including a ratio of training data to validation data and “validating” using validation metrics. The ratio adds an abstract mathematical relationship, and “satisfies one or more validation metrics applied to validation data” adds abstract mathematical calculations to the claim. As explained in MPEP 2106.05(f), implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. Even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 1, 8, and 15 are thus directed to an abstract idea.
Under Step 2B of the SME analysis, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the 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).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) individually and in combination are merely being used to apply the abstract idea to a general computer components. For the same reason, the elements are not sufficient to provide an inventive concept. As explained in MPEP 2106.05(f), implementing an abstract idea with a generic computer does not add significantly more in Step 2B. Accordingly the additional elements fail to provide significantly more than the abstract idea itself, meaning claims 1, 8, and 15 are ineligible.
Dependent claim(s) 2, 4, 5, 6-7, 9, 11-14, 16, and 18-20 do not aid in the eligibility of the independent claims. These claims merely further define the abstract idea without reciting any further additional elements. Claims 7 and 4, specifically, recites additional abstract activity belonging to the mathematical concepts grouping of abstract ideas (“generating ... a confidence interval ... from an output of the machine learning model”). Thus dependent claims 2, 4, 5, 6-7, 9, 11-14, 16, and 18-20 are also ineligible.
Dependent claims 3, 10, and 17 recite additional elements including: retrieving, by the one or more processors from one or more networks, vehicle seat data. Similar to the additional elements identified above, the processors, network, and retrieving are described in ordinary terms and merely used as a tool in performance of the abstract idea. This limitations add extra-solution activity to the claim in the form of mere data-gathering, which does not provide integration in prong two or an inventive concept in step 2B. See MPEP 2106.05(g). Furthermore, receiving data over a network has been repeatedly considered a well-understood, routine, and conventional computer activity by the Courts (see MPEP 2106.05(d)). Accordingly, claims 3, 10, and 17 are ineligible.
Claim Rejections - 35 U.S.C. § 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.
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 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 factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 8-9, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Yang (US 2014/0244428 A1) in view of Nygaard et al. (US 2015/0088684 A1), and further in view of Raj et al. (US 2024/0070743 A1).
Claim 1
Yang discloses a computer-implemented method for using machine learning to generate personalized recommendations for one or more products, the method comprising:
training, by the one or more processors, a machine learning model for recommending one or more products using a set of characteristics of previously recommended products (¶ [0041] “If the user selects one or more products through presentation apparatus 112, analysis apparatus 108 may provide the user's selection of products to the ranking engine to update the behavior of the ranking engine. For example, analysis apparatus 108 may add the selection to a set of historical data that is used to train the ranking engine”; ¶ [0050] “The historical data may then be used to train and/or update ranking engine 202 if ranking engine 202 is implemented using a machine-learning technique.”); ...
receiving, by the one or more processors, input data (¶ [0048] “user data 208 for the user is provided to ranking engine 202”);
determining, by the one or more processors, a set of characteristics of the input data (¶ [0048] “For example, user data 208 may include a user type, user context, and/or user profile for the user”);
generating, by the trained machine learning model using the set of characteristics of the input data, a product recommendation score for the one or more products (¶ [0049] “For example, ranking engine 202 may apply a number of rules and/or statistical techniques to classify the user based on user data 208 and assign a score to each product according to the relevance, desirability, and/or applicability of the product to the user.”);
ranking, by the one or more processors, the one or more products by product recommendation score to generate a product recommendation list (¶ [0049] “ Ranking engine 202 may then sort the products in decreasing order of score and provide a subset and/or complete list of the sorted products as ranked products 210”), wherein the product recommendation includes all products ranked above a threshold (¶ [0040] “For example, presentation apparatus 112 may display features 118 related to the top three ranked products 114 to the user within a user interface ... and omit information related to lower ranked products in the list from the user interface”); and
presenting, by the one or more processors, the product recommendation list to a client device (¶ [0040] “presentation apparatus 112 may display features 118 related to the top three ranked products 114 to the user within a user interface”).
Yang does not disclose recommending one or more vehicle seats or receiving, by one or more processors, a request for a vehicle seat recommendation. However, Nygaard et al. – which is also directed to providing product recommendations, teaches: recommending one or more vehicle seats (¶ [0069]) and receiving, by one or more processors, a request for a vehicle seat recommendation (Nygaard ¶ [0069] “This change of topic may be detected as a request for a recommendation for an infant car seat.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have substituted the product of Yang for the vehicle seat product (car seat), as taught by Nygaard. Nygaard describes recommending various products (¶¶ [0068]-[0069]), thus one of ordinary skill in the art would have been able to carry out such a substitution, and the results were reasonably predictable.
The combination of Yang in view of Nygaard does not explicitly disclose limitations associated with validating a trained machine learning model, however Raj – which is also directed to generating recommendations using a machine-learning model – further teaches:
wherein the set of characteristics of previously recommended products includes a ratio of training data to validation data (Raj ¶ [0043] “In some embodiments, a portion of the training data may be withheld during training and/or used to validate the trained machine-learning model, e.g., may be used to compare the output of the trained model with the ground truth for that portion of the training data to evaluate an accuracy of the trained model”)
validating, by the one or more processors, a trained machine learning model, wherein the trained machine learning model is validated by a determination that the trained machine learning model satisfies one or more validation metrics applied to the validation data (Raj ¶ [0043] “validate the trained machine-learning model, e.g., may be used to compare the output of the trained model with the ground truth for that portion of the training data to evaluate an accuracy of the trained model”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the validating as taught by Raj in the method of Yang in order to improve the scoring by the model (Raj ¶ [0102]).
Claim 2
Yang further discloses, wherein:
(i) the set of characteristics include one or more of child data, vehicle data, current vehicle seat data, location data (see ¶ [0036] for user data including location), vehicle interior parameters data, vehicle seat parameters data, on- market vehicle seat data, vehicle seat reviews, vehicle seat prices, or vehicle seat stock information,
(ii) the input data includes one or more of child data, vehicle data, current vehicle seat data, or location data,
(iii) the child data includes one or more data of child age, child height, or child weight,
(iv) the vehicle data includes one or more of vehicle year, vehicle make, or vehicle model,
(v) the current vehicle seat data includes one or more of vehicle seat brand, vehicle seat product name, or vehicle seat serial number,
(vi) the location data includes one or more of geolocation of the requestor, address of the requestor, or zip code of the requestor (see ¶ [0036] for demographic information including location, Examiner notes zip codes are common for demographic analysis),
(vii) the vehicle interior parameters data includes one or more data of dimensions of an interior of the vehicle or dimensions of a seat area of the vehicle,
(viii) the vehicle seat parameters data includes one or more of dimensions of the vehicle seat or weight limit of the vehicle seat, and
(ix) on-market vehicle seat data includes one or more of list of one or more vehicle seat manufacturers, list of one or more vehicle seats per manufacturer, dimensions of one or more vehicle seats per manufacturer, or weight limit of one or more vehicle seats per manufacturer.
Claim 6
Yang further discloses, wherein training the machine learning model comprises:
recommending, by the one or more processors, a product based upon a set of previously recommended products and the set of characteristics of the previously recommended products (¶ [0041] “analysis apparatus 108 may add the selection to a set of historical data that is used to train the ranking engine and/or update the ranking of products by the ranking engine”); and
determining, by the one or more processors, a prior requestor selected the recommended product (¶ [0041]; ¶ [0050]).
Claims 8 and 13, which are directed to a system, recite limitations that are parallel in nature as those addressed above for method claims 1 and 6. Claim(s) 8 and 13 are therefore rejected for the same reasons as set forth above for claims 1 and 6, respectively.
Claims 15, which is directed to a non-transitory computer-readable medium, recite limitations that are parallel in nature as those addressed above for method claim 1. Claim(s) 15 is therefore rejected for the same reasons as set forth above for claim 1.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Nygaard and Raj, and further in view of Goffin et al. (US 2019/0122275 A1).
Claim 3
Yang further discloses, wherein determining the set of characteristics of the input data comprises:
retrieving, by the one or more processors from one or more networks, product data including one or more of on-market product data, product reviews, product prices, or product stock information (see ¶ [0052] “testimonials and/or reviews of product 302 by the user’s contacts).
As discussed above, the Examiner is modifying the product of Yang to include a car seat, as taught by Nygaard. The combination of Yang in view of Nygaard, and further in view of Raj does not teach limitations associated with vehicle interior parameters or vehicle seat parameters, however Goffin – which is also directed to recommendations – teaches:
determining, by the one or more processors, the vehicle interior parameters based upon the input data (Goffin ¶ [0018]);
determining, by the one or more processors, the vehicle seat parameters based upon the input data (Goffin ¶ [0018]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the vehicle parameters as taught by Goffin in the method of Yang in order to advantageously allow for a consumer to evaluate the packability of product relative to a vehicle, even prior to purchase of the product (Goffin ¶ [0002]).
Claim 10 and 17, which are directed to system and manufacture, recite limitations that are parallel in nature as those addressed above for method claim 3. Claim(s) 10 and 17 are therefore rejected for the same reasons as set forth above for claim 3.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Nygaard and Raj, and further in view of Donnels et al. (US 2022/0309557 A1).
Claim 5
The combination of Yang in view of Nygaard, and further in view of Raj does not teach limitations associated with locations of stores, or presenting sorted stores. However, Donnels – which is also directed to generating recommendations – teaches, further comprising:
determining, by the one or more processors, locations of one or more stores selling the one or more products based upon product stock information (¶¶ [0045]-[0046]);
identifying, by the one or more processors, the one or more stores closest in position to input location data (¶ [0046]);
sorting, by the one or more processors, the one or more stores based upon the product recommendation list and the product stock information (¶ [0046]);
presenting, by the one or more processors, the one or more sorted stores to the client device (¶ [0046]; Fig. 4 #450).
As discussed above, the Examiner is relying on Nygaard to show that the “product” of Yang can include a car seat, as claimed. By the same rationale, the Examiner is also modifying the “product” of Donnels for the car seat of Nygaard. Regarding Donnels, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the store locations as taught by Donnels in the method of Yang in view of Nygaard in order to improve existing electronic marketplace applications, search engines, and corresponding user interfaces by generating one or more user interface elements or personalized pages based on determining that a set of items are within a geographical vicinity of a user (Donnels ¶ [0002]).
Claims 12 and 19 which are directed to system and manufacture, recite limitations that are parallel in nature as those addressed above for method claim 5. Claim(s) 12 and 19 are therefore rejected for the same reasons as set forth above for claim 5.
Response to Arguments
Applicant's arguments filed 01/28/2026 with respect to the 35 U.S.C. § 101 rejections of claim 1-20 have been fully considered but they are not persuasive.
On page 11 of the Remarks, Applicant argues “the claims do not identify concepts related to commercial or legal interactions”. The Examiner respectfully disagrees.
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. MPEP § 2106.04(II)(1). To facilitate examination, the Office has set forth an approach to identifying abstract ideas that distills the relevant case law into enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types.
The previous Office Action explains that the instant claims recite abstract activity of “recommending one or more vehicle seats”, and “recommending products (vehicle seats) to a user directly facilitates or drives purchasing decisions, exemplifying a sales activity” (pgs. 3-4). As explained in MPEP 2106.04, the phrase "methods of organizing human activity" is used to describe concepts relating to commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations). The Examiner has identified the recited abstract idea and provided rationale as to why the claims describe concepts related to an enumerated grouping of abstract ideas (i.e. certain methods of organizing human activity), thus satisfying step 2A prong II. As explained in the MPEP excerpt provided above, examiners are no longer required to rely on individual cases when determining an abstract idea but to generally apply case law by relying on the enumerated groupings of abstract ideas.
On page 13 of the Remarks, Applicant argues “independent claims 1, 8, and 15, at a minimum, recite additional elements beyond the judicial exception (certain methods of organizing human activity), which integrate the alleged judicial exception into a practical application”. Specifically, Applicant argues “it is by validating the trained machine learning model by a determination that the trained machine learning model satisfies one or more validation metrics applied to the validated data which reflects an ‘improvement to how the machine learning model itself operates.’ See Ex parte Desjardins.”
The Examiner respectfully disagrees. If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. See MPEP 2106.05(a).
Neither Applicant’s Specification nor claims reflect or describe an improvement to how the machine learning model itself operates. Applicant points to ¶ [0006] of the Specification which describes problems in conventional techniques for selecting an appropriate seat and ¶ [0071] which explains that the machine learning model is continually trained until satisfying a validation metric. However, neither these paragraphs nor the claims describe problems in conventional machine-learning techniques or solutions thereof. Validating a machine-learning model is a foundational aspect of machine-learning technology. Claim 1 recites, "validating, by the one or more processors, a trained machine learning model, wherein the trained machine learning model is validated by a determination that the trained machine learning model satisfies one or more validation metrics applied to the validation data." Instead of disclosing “a specific implementation of a solution to a problem in the software arts,” Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016), or “a specific means or method that solves a problem in an existing technological process,” Koninklijke, 942 F.3d at 1150, the only thing the claims disclose about the validation method is that a validation metric is applied. This is a well-understood or “traditional” approach in model validation (see Gullapudi et al. US 2023/0214456 A1 ¶ [0044] “In a traditional approach, accuracy values are calculated for every unique confidence (i.e., probability output by a ML model) in the validation sub-set during the training pipeline” in view of ¶ [0041] “In the validation phase, the (trained) ML model is evaluated using the validation sub-set). Invoking a computer to perform an existing process or invoking a computer as a tool used in its ordinary capacity does not integrate a judicial exception into a practical application or provide significantly more.
For at least these reasons, the Examiner is maintaining the 35 U.S.C. § 101 rejections of claims 1-20.
Applicant’s arguments with respect to the 35 U.S.C. § 103 rejections of claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
J. Chen, Z. Zhao, J. Shi and C. Zhao (NPL Reference U) is directed to improving the effect of Top-N recommendation models.
Applicant's amendment necessitated the new 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 KENNEDY A GIBSON-WYNN whose telephone number is (571)272-8305. The examiner can normally be reached M-F 8:30-5:30 PM.
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/K.G.W./ Examiner, Art Unit 3688
/Jeffrey A. Smith/ Supervisory Patent Examiner, Art Unit 3688
Prosecution Insights