Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action to Application Serial Number 18/490,650, filed on October 19, 2023. In response to Examiner’s Non-Final Office Action of September 30, 2025, Applicant, on December 18, 2025, amended no claims. Claims 1, 4-9, 11, and 14-20 are pending in this application and have been rejected below.
Acknowledgement is hereby made of receipt of Information Disclosure Statements filed by Applicant on 12/08/2023, 4/8/2024, 6/8/2024, 8/6/2024,10/10/2024, 2/7/2025, 4/10/2025 and 10/16/2025 and 2/8/2026. 1449' s are attached. Examiner notes that due to the excessively voluminous Information Disclosure Statement submitted by Applicant, the Examiner has given only a cursory review of the listed references. In accordance with MPEP 609.04(a), Applicant is encouraged to provide a concise explanation of why the information is being submitted and how it is understood to be relevant. Concise explanations (especially those which point out the relevant pages and lines) are helpful to the Office, particularly where documents are lengthy and complex and Applicant is aware of a section that is highly relevant to patentability or where a large number of documents are submitted and applicant is aware that one or more are highly relevant to patentability. See MPEP.2004 Aids to Compliance With Duty of Disclosure
"13. It is desirable to avoid the submission of long lists of documents if it can be avoided. Eliminate clearly irrelevant and marginally pertinent cumulative information. If a long list is submitted, highlight those documents which have been specifically brought to applicant’s attention and/or are known to be of most significance. See Penn Yan Boats, Inc. v. Sea Lark Boats, Inc., 359 F. Supp. 948, 175 USPQ 260 (S.D. Fla. 1972), aff ’d, 479 F.2d 1338, 178 USPQ 577 (5th Cir. 1973), cert. denied, 414 U.S. 874 (1974). But cf. Molins PLC v. Textron Inc., 48 F.3d 1172, 33 USPQ2d 1823 (Fed. Cir. 1995)."
Priority
The Examiner has noted this Application is a Continuation of Application 18/179, 234 filed March 6, 2023 which is a Continuation of Application 17/849,355 filed June 24, 2022, which is a Continuation of Application 16/821,329 filed March 17, 2020.
Response to Arguments
Applicant’s arguments filed December 18, 2025 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed December 18, 2025.
On page 9-12 of the Remarks regarding 35 U.S.C. § 101, Applicant states the rejection should be withdrawn at least because the Office Action fails to show the claims "recite" an abstract idea under Step 2A, Prong One of the USPTO framework for administering the Alice/Mayo test. In response, Examiner has clearly stated the abstract idea below . The abstract idea includes generating a recommendation for the recommended amount of consideration; effectuating presentation of the recommendation associated with the content creator;… receiving input conveying acceptance of the recommended amount of consideration for the first benefit item; conveying the acceptance of the recommended amount of consideration: updating the information characterizing the first benefit item to show that the requested amount of consideration associated with the first benefit item has automatically been changed to the recommended amount of consideration; and updating a creator page associated with the content creator to display the recommended amount of consideration as a current offer; which is within the Abstract idea grouping of “Methods of Organizing Human Activity” – managing interactions; marketing activities. Examples of Methods of Organizing Human Activities include commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
The recitation of “system”, “physical processor”, “machine-readable instructions”, “user interface” and “platform”, provide nothing in the claim elements to preclude the step from being “Methods of Organizing Human Activity”- managing interaction; marketing activities. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “system”, “physical processor”, “machine-readable instructions”, “user interface” and “platform” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f).
The general use of a machine learning analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea. The general use of a machine learning analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
Examiner asserts when performing the § 101 analysis, Examiner did consider each claim and every limitation, both individually and in combination as according to the PTO's guidelines for § 101 eligibility. Please see 101 analysis below for additional detail.
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, 4-9, 11, and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 4-9, 11, and 14-20 are directed to recommend price of benefits obtained by consumers through an online membership platform (price benefit recommendation as stated in Par. 01 of Applicant’s specification.).
Claim 1 and Claim 9 recite a system for price benefit recommendation, Claim 11 and Claim 19 recite a method for price benefit recommendation, which include generate a recommendation for the recommended amount of consideration; effectuate presentation of the recommendation associated with the content creator, receive input conveying acceptance of the recommended amount of consideration for the first benefit item; conveying the acceptance of the recommended amount of consideration: update the information characterizing the first benefit item to show that the requested amount of consideration associated with the first benefit item has automatically been changed to the recommended amount of consideration; and update a creator page associated with the content creator to display the recommended amount of consideration as a current offer; and provide the trained machine learning model the information characterizing the first benefit item as updated … (Claim 1). Provide a trained machine learning model with information characterizing a first benefit item associated with a content creator hosted by an online membership platform; generate, using output of the trained machine learning model, a recommendation conveying a recommended amount of consideration for the first benefit item that corresponds to greater consumption by the set of consumers; automatically set an offered amount of consideration associated with the first benefit item to the recommended amount of consideration; and automatically update a creator page associated with the content creator to display the recommended amount of consideration as a current offer; and provide the trained machine learning model with information indicating that the offered amount of consideration associated with the first benefit item has been automatically set to the recommended amount of consideration to refine the trained machine learning model (Claim 9). Output a recommended amount of consideration for a first benefit item of a content creator, the recommended amount of consideration corresponding to greater consumption by the set of the consumers and being different from a requested amount of consideration associated with the first benefit item initially requested by the content creator; and generate a recommendation for the recommended amount of consideration; effectuating presentation of the recommendation associated with the content creator; receive input conveying acceptance of the recommended amount of consideration for the first benefit item; conveying the acceptance of the recommended amount of consideration: updating the information characterizing the first benefit item to show that the requested amount of consideration associated with the first benefit item has automatically been changed to the recommended amount of consideration; and updating a creator page associated with the content creator to display the recommended amount of consideration as a current offer; and providing the trained machine learning model the information characterizing the first benefit item as updated to refine the trained machine learning model (Claim 11). Providing a trained machine learning model with information characterizing a first benefit item associated with a content creator hosted by an online membership platform, the trained machine learning model having been trained based on consumption of benefit items at requested amounts of consideration by a set of consumers through the online membership platform; generating a recommendation conveying a recommended amount of consideration for the first benefit item that corresponds to greater consumption by the set of consumers; responsive to acceptance of the recommendation by the content creator: automatically setting an offered amount of consideration associated with the first benefit item to the recommended amount of consideration; and automatically updating a creator page associated with the content creator to display the recommended amount of consideration as a current offer; and providing the trained machine learning model with information indicating that the offered amount of consideration associated with the first benefit item has been automatically set to the recommended amount of consideration to refine the trained machine learning model (Claim 19).
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Methods of Organizing Human Activity” – managing interactions; marketing activities. The recitation of “system”, “physical processor”, “machine-readable instructions”, “user interface” and “platform”, provide nothing in the claim elements to preclude the step from being “Methods of Organizing Human Activity”- managing interaction; marketing activities. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “system”, “physical processor”, “machine-readable instructions”, “user interface” and “platform” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f).
Furthermore, the claim 1, claim 9, claim 11 and claim 19 recite using one or more machine learning analysis techniques. The specification discloses the machine learning analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element 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, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in pricing analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “system”, “physical processor”, “machine-readable instructions”, “user interface” and “platform” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 4-8, and 20 recite wherein the consumption of the benefit items at the requested amounts of consideration by the set of the consumers is characterized based on quantity of consumers who have consumed the benefit items at the requested amounts of consideration; wherein the recommended amount of consideration is an amount associated with having a largest quantity of consumers who have consumed the benefit items; wherein the benefit items are characterized by benefit type; wherein the recommended amount of consideration is for the benefit items of a first benefit type, the first benefit item being of the first benefit type; wherein the first benefit type corresponds to a medium of creation; the recommendation is displayed during registration of a user account of the content creator with the online membership platform; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 9, 11 and 19. Regarding Claim 20 and the additional element of “platform” – it is receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information.
Reasons Claims are Patentably Distinguishable from the Prior Art
Examiner analyzed Claims 1, 4-9, 11, and 14-20 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below.
In regards to Claim 1 and Claim 9 (similarly Claim 11 and Claim 19), the prior art does not teach or fairly suggest:
“… train a machine learning model to generate a trained machine learning model, the machine learning model being trained based on information characterizing benefit items obtained by consumers through an online membership platform, the online membership platform hosting content creators of the benefit items, the information further characterizing consumption of the benefit items at requested amounts of consideration by a set of the consumers, the trained machine learning model being configured to output a recommended amount of consideration for a first benefit item of a content creator, the recommended amount of consideration corresponding to greater consumption by the set of the consumers and being different from a requested amount of consideration associated with the first benefit item initially requested by the content creator”
… and responsive to obtaining input by the content creator into a user interface conveying acceptance of the recommended amount of consideration: update the information characterizing the first benefit item to show that the requested amount of consideration associated with the first benefit item has been changed to the recommended amount of consideration; and provide the trained machine learning model the information characterizing the first benefit item as updated to refine the trained machine learning model.”
… generate, using output of the trained machine learning model, a recommendation conveying a recommended amount of consideration for the first benefit item that corresponds to greater consumption by the set of consumers; responsive to acceptance of the recommendation by the content creator via input into a user interface: automatically set an offered amount of consideration associated with the first benefit item to the recommended amount of consideration; and provide the trained machine learning model with information indicating that the offered amount of consideration associated with the first benefit item has been automatically set to the recommended amount of consideration to refine the trained machine learning model.”
Examiner finds that Riesman et al. (U.S. PG Publication 20110295722) teaches a system, apparatus, and method for collecting feedback on transactions involving particular buyers, with respect to the buyer, and then used to facilitate the process of pricing future transactions involving that buyer (see Abstract). In particular, Riesman discloses prices can optionally be set by buyers based on a perception of value received, with consideration to problems such as quality of the item and/or related support services. Reflecting that context, a wide variety of data and analytic methods can be useful in informing and understanding specific FP price-setting behaviors to reflect such considerations. Such methods can optionally be applied by buyers and sellers, alike.. (see par. 0206-208), determination of whether to accept a FP offer can be automated to any desired degree, and such automation can be particularly desirable for routine purchases (see par. 0131-136) and analysis can optionally be used by seller/support systems to decide on future FP Offers to consumers, 301, based on algorithmic, rule-based data processing, analysis and decision procedures) (see par. 0061).
Jin et al. (U.S. PG Publication. 20200020014) teaches method and apparatus that assesses an acquisition price of a subscription product by executing an artificial intelligence (AI) algorithm or a machine learning algorithm in a 5G environment connected for the Internet of Things, and that reinforces a trained model by reflecting, as a reward, a result of suggesting to a user to acquire the product. A product price assessing method according to one embodiment of the present disclosure may include: applying at least one of user information, product information, or environment information, or preprocessed data thereof to a machine learning-based first trained model; and assessing a product price of a product related to the product information based on the first trained model. Reinforcement learning may be conducted to the first trained model by reflecting, as a reward, whether the user determines to acquire the product at the assessed product price. (see Abstract). In particular, Jin discloses transmit, to a user terminal, a trained model reinforced by reflecting, as a reward, whether a user determines to acquire a product at an acquisition price assessed via the trained model, may cause the user terminal to assess an acquisition price of a subscription product and to suggest the assessed acquisition price to the user, and may receive the result thereof from the user terminal. (see par. 0038-0039, 0283-284).
Ariche (U.S. PG Publication 20090287527) teaches an online marketplace generates a price curve for the item to describe the likelihood to sell as a function of price. Based on seller preference data, an analysis of historical data of listings, or both, the online marketplace estimates the utility preferences for the seller account, such as the cost of time. Using the utility preferences and the price curve for an item, the online marketplace can generate a utility curve that estimates the utility for the seller account as a function of the item price. Using the utility curve, the online marketplace generates utility-based price guidance for a particular seller of the item. A user interface presents the utility-based price guidance to the seller and enables the seller to set the price of the item based on the price guidance (see Abstract). In particular, Ariche discloses The machine learning system 130 may access listing data from the e-commerce machine 120 and use the accessed data as training data for one or more machine learning algorithms. For example, one machine learning algorithm may be used to predict when a listing of an item will sell, based on the item and a listed price of the item. As another example, a machine learning algorithm may be used to predict, for a particular seller, a preferred listing duration (i.e., a preferred time-to-sell for items). (see par. 0021v-0022).
Although Riesman, Jin and Ariche teach the pricing analysis elements of the claim, none of the cited prior art, singularly or in combination, teach or fairly suggest, the combination of, the consumption analysis and machine learning model training and updating analysis to recommend price and/or amount given user acceptance.
The dependent claims 4-8, and 14-18, and 20 are eligible under 35 U.S.C. 102 and 35 U.S.C. 103 because they depend on claim 1, 9, 11 and 19 that is determined to be eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: : US Patent No. 11838591B2 to Karlin et al. – Col 15 Ln25-40-In some embodiments, the subject matter of the recommendations discussed herein may refer to any potential goods and/or services and may be identified by a serial number or product SKU. For example, the system may generate a media asset recommendation featuring the serial number or product SKU. Each good and/or service may have one or more associated user-supplied criteria. These criteria may include user preferences of a user such as a preferred genre, length, type, feature character, featured actor, etc. Additionally or alternatively, a potential user criteria may indicate: (a) maximum amount willing to pay; (b) condition of good and/or service required;”).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
Another resource that is available to applicants is the Patent Application Information Retrieval (PAIR). Information regarding the status of an application can be obtained from the (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAX. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, please feel free to contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/Examiner, Art Unit 3624