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 communications filed on June 14, 2024. The applicant’s claim for benefit of provisional application 63521162, filed June 15, 2023, has been received and acknowledged.
Claims 1-20 are currently pending and have been examined.
Examiner’s Note: The Examiner notes that claims 1-20 do not recite any of the judicial exceptions enumerated in the MPEP and thus are patent eligible under 35 USC 101.
Claim Rejections - 35 USC § 112
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 1-20 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.
Claims 1-20: Claim 1 recites “that indicates a higher user engagement or satisfaction.” The term “higher” is a relative term which renders the claim indefinite. The term “higher” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Thus, the term “user engagement or satisfaction” is rendered indefinite by use of the term “higher.”
Claims 10 and 19 are rejected for similar reasons.
Claims 2-9, 11-18, and 20 inherit the deficiencies of claims 1, 10, and 19.
Claims 9 and 18: Claim 9 recites “determining placements of the plurality of products further based on linearity of the supply-demand curves of the plurality of products.” The metes and bounds of this claim are unclear because a person having ordinary skill in the art would be unable to determine how to determine placements of the products based on the linearity of the supply-demand curves. For purposes of examination, the Examiner is interpreting this portion of claim 9 as reciting “determining placements of the plurality of products based on the supply-demand curves of the plurality of products.”
Claim 18 is rejected for similar reasons.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 10-13, and 19-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2015/0278837 A1 to Lahav et al (hereinafter “Lahav”).
Claims 1, 10, and 19: Lahav discloses “methods and systems for predicting the online behavior of one or more web users.” (See Lahav, at least para. [0002]). Lahav further discloses a processor system comprised of one or more processors; and a non-transitory computer-readable storage medium comprising stored instructions thereon (See Lahav, at least para. [0009], one or more data processors and a non-transitory computer readable storage medium containing instructions which when executed on the one or more data processors, cause the one or more data processors to perform all or part of a method). Lahav further discloses:
collecting user data describing how a plurality of users interact with an online system (See Lahav, at least paras. [0031]-[0032], for each of the one or more users, behavior prediction engine collects information about the user and/monitors the user’s interactions with one or more web pages; information includes which web pages the user has visited, when they were visited, which online purchases were made, demographic information, time spent interacting with the web pages, etc.);
for each of the plurality of users (See Lahav, at least paras. [0031]-[0032], for each of the one or more users, behavior prediction engine collects information about the user and/monitors the user’s interactions with one or more web pages; information includes which web pages the user has visited, when they were visited, which online purchases were made, demographic information, time spent interacting with the web pages, etc.),
applying reinforcement learning to the user data of a corresponding user to train a digital twin model that predicts user interactions in any given virtual environment variant (See Lahav, at least paras. [0033]-[0040], different user groups are defined and users are assigned to each group; for each group, the behavior prediction engine can generate and/or update a model that predicts a likelihood that a user will make a purchase, that a user will accept a chat invitation, that a user will clock on or partly or fully watch a video, or will predict a particular type or value of a purchase; para. [0042], each model can be generated or updated using data of previous user visits; para. [0043], model combination combines results from all the models to generate an overall prediction);
configuring a plurality of virtual environment variants, each differing in at least one of layout, product placement, navigational element, or promotional strategy (See Lahav, at least para. [0023], webpage can be customized in a number of ways including by offering a chat invitation or providing a discount; para. [0061], chat invitation, discounts, video or animation);
executing the digital twin model across the plurality of virtual environment variants to simulate user interactions with each of the plurality of virtual environment variants (See Lahav, at least paras. [0033]-[0040], different user groups are defined and users are assigned to each group; for each group, the behavior prediction engine can generate and/or update a model that predicts a likelihood that a user will make a purchase, that a user will accept a chat invitation, that a user will click on or partly or fully watch a video, or will predict a particular type or value of a purchase);
for each of the plurality of virtual environment variants,
logging the user interactions simulated by the digital twin model in the virtual environment variant (See Lahav, at least para. [0041], each model can be generated or updated using data of previous user visits; user is assigned to a class (for example, based on whether or not a chat was offered and accepted or offered and declined); and
analyzing the logged user interactions to determine a set of engagement metrics associated with the virtual environment variant (See Lahav, at least para. [0041], group-specific model relates user characteristics to predict an outcome or action (for example, whether a purchase was made; para. [0025], users who are offered and accept a chat invitation have a 90% probability of making purchase while user who are offered and decline a chat invitation have a 30% probability of making a purchase);
comparing the sets of engagement metrics associated with the plurality of virtual environment variants to identify a set of engagement metrics that indicates a higher user engagement or satisfaction (See Lahav, at least para. [0025], users who are offered and accept a chat invitation have a 90% probability of making purchase while user who are offered and decline a chat invitation have a 30% probability of making a purchase; model combination can weight the two models based on data size used to develop/verify each model, past accuracy, probability that each model applies to the user, etc.; model combination products a combined result biased toward the result of the second model) ;
selecting a virtual environment variant associated with the identified set of engagement metrics that indicates the higher user engagement or satisfaction (See Lahav, at least para. [0025], rule indicates that a chat is to be offered in response to any request associated with a combined result above 60%; in this case, the combined result is less than 60% so a chat would not be offered; paras. [0062]-[0063], model combination in this example generates probabilities for first user indicating ahigh probability of completing a purchase and for second user indicating a low probability of completing a purchase; webpage provider has a rule of offering a real-time chat opportunity to users associated with high probabilities; chat opportunity is presented to first user but not the second); and
providing for display of the selected virtual environment variant to the user (See Lahav, at least para. [0025], chat opportunity is presented to first user).
Claims 10 and 19 are rejected for similar reasons.
Claims 2, 11, and 20: Lahav further discloses wherein training the digital twin model comprises:
conducting experiments on the corresponding user (See Lahav, at least para. [0042], each model can be generated or updated using data of previous user visits; user is assigned to a class (for example, based on whether or not a chat was offered and accepted or offered and declined);
collecting user data associated with the experiments (See Lahav, at least paras. [0031]-[0032], for each of the one or more users, behavior prediction engine collects information about the user and/monitors the user’s interactions with one or more web pages; information includes which web pages the user has visited, when they were visited, which online purchases were made, demographic information, time spent interacting with the web pages, etc.); and
training the digital twin based on the collected user data (See Lahav, at least para. [0042], each model can be generated or updated using data of previous user visits).
Claims 11 and 20 are rejected for similar reasons.
Claims 3 and 12: Lahav further discloses wherein conducting the experiments comprises:
presenting a diverse set of virtual environment variants to the corresponding user (See Lahav, at least paras. [0031]-[0032], for each of the one or more users, behavior prediction engine collects information about the user and/monitors the user’s interactions with one or more web pages; information includes which web pages the user has visited, when they were visited, which online purchases were made, demographic information, time spent interacting with the web pages, etc.; least para. [0042], each model can be generated or updated using data of previous user visits; user is assigned to a class (for example, based on whether or not a chat was offered and accepted or offered and declined)); and
collecting user data associated with user interactions with the diverse set of virtual environment variants (See Lahav, at least para. [0032], for each of the one or more users, behavior prediction engine collects information about the user and/monitors the user’s interactions with one or more web pages; information includes which web pages the user has visited, when they were visited, which online purchases were made, demographic information, time spent interacting with the web pages, etc.; para. [0042], group-specific model relates user characteristics to predict an outcome or action (for example, whether a purchase was made; para. [0025], users who are offered and accept a chat invitation have a 90% probability of making purchase while user who are offered and decline a chat invitation have a 30% probability of making a purchase).
Claim 12 is rejected for similar reasons.
Claims 4 and 13: Lahav further discloses:
collecting new user data of the corresponding user associated with user interaction with the selected virtual environment variant (See Lahav, at least para. [0042], each model can be generated or updated using data of previous user visits; para. [0032], for each of the one or more users, behavior prediction engine collects information about the user and/monitors the user’s interactions with one or more web pages; information includes which web pages the user has visited, when they were visited, which online purchases were made, demographic information, time spent interacting with the web pages, etc.); and
retraining the digital twin based on the collected new user data (See Lahav, at least para. [0042], each model can be generated or updated using data of previous user visits).
Claim 13 is rejected for similar reasons.
Claims 5 and 14: Lahav further discloses:
receiving a selection of a goal among a plurality of goals from a user (See Lahav, at least paras. [0044]-[0045], client such as an ecommerce company provides policies to influence how users are classified, predictions, etc.; client may identify multiple purchasing probability ranges and may provide a functionality such as a chat or a discount that is only offered to a subset of the ranges); and
selecting the virtual environment variant from the plurality of virtual environment variants further based on the goal of the user (See Lahav, at least paras. [0044]-[0045], client such as an ecommerce company provides policies to influence how users are classified, predictions, etc.; client may identify multiple purchasing probability ranges and may provide a functionality such as a chat or a discount that is only offered to a subset of the ranges).
Claim 14 is rejected for similar reasons.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
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 6-9 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Lahav as applied to claims 1 and 10 above, and further in view of US 2023/0206265 A1 to Girija et al. (hereinafter “Girija”).
Claims 6 and 15: Lahav discloses all the limitations of claims 1 and 10 discussed above.
Lahav does not expressly disclose training a supply-demand prediction model using the collected user data, the supply-demand prediction model is trained to receive features of a product as input to generate a supply-demand curve of the product; applying the supply-demand prediction model to a plurality of products listed on the online system to determine a supply-demand curve for each of the plurality of products; and generating the plurality of virtual environment variants based on the supply-demand curves of the plurality of the products.
However, Girija discloses an “optimized dynamic pricing engine 100, in accordance with an embodiment. The engine 100 may include various modules to implement the embodiments presented herein. In an embodiment, the optimized dynamic pricing engine 100 may include modules such as, but not limited to, a baseline price computation engine 102, a profile manager module 104, an Artificial Intelligence and Machine Leaning (AI & ML) modelling engine 106, a priority resets module 108, a business rules manager 110, a fraud scoring computation engine 112, a listing prioritization and personalization engine 114, a personalized price generation engine 116, a dynamic content adaptation engine 118.” (See Girija, at least para. [0022]). Girija further discloses:
training a supply-demand prediction model using the collected user data, the supply-demand prediction model is trained to receive features of a product as input to generate a supply-demand curve of the product (See Girija, at least para. [0036], Modelling Engine predicts a supply and demand for all bookable products using known AI & ML algorithms; bookable products may include products for which inventory may be available with a seller/vendor and the user may be interested in purchasing it (e.g. the products being browsed by the user); input includes market data including competitor details such as prices, reviews, and industry data and user profile details from the profile manager module; based on these received values, the AI & ML modelling engine may determine supply and demand predictions; para. [0037], Modelling Engine may implement self-learning algorithms to improve these modelling algorithms based in real-time);
applying the supply-demand prediction model to a plurality of products listed on the online system to determine a supply-demand curve for each of the plurality of products (See Girija, at least para. [0036], Modelling Engine predicts a supply and demand for all bookable products using known AI & ML algorithms; bookable products may include products for which inventory may be available with a seller/vendor and the user may be interested in purchasing it (e.g. the products being browsed by the user); input includes market data including competitor details such as prices, reviews, and industry data and user profile details from the profile manager module; based on these received values, the AI & ML modelling engine may determine supply and demand predictions; para. [0038], The AI & ML modelling engine 106 may provide the pricing range that is appropriate for meeting business objectives); and
generating the plurality of virtual environment variants based on the supply-demand curves of the plurality of the products (See Girija, at least para. [0047], listing prioritization and personalization engine generates a personalized relevance score for each bookable product and an associated user; para. [0050], listing prioritization and personalization engine may determine a quantitative value for each of the above criteria and may determine the relevance score based on the summation of values of all the criteria to determine the relevance score for each parking lot displayed to the user. The outcome of the listing prioritization and personalization engine along with the business rules determines the products that would be displayed to the user and the display position of each of such products).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the online behavior prediction system and method of Lahav the ability of training a supply-demand prediction model using the collected user data, the supply-demand prediction model is trained to receive features of a product as input to generate a supply-demand curve of the product; applying the supply-demand prediction model to a plurality of products listed on the online system to determine a supply-demand curve for each of the plurality of products; and generating the plurality of virtual environment variants based on the supply-demand curves of the plurality of the products as disclosed by Girija since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so in order to optimize prices of products for a specific customer. (See Girija, at least para. [0003].
Claim 15 is rejected for similar reasons.
Claims 7 and 16: The combination of Lahav and Girija discloses all the limitations of claims 6 and 15 discussed above.
Lahav does not expressly disclose wherein generating the plurality of virtual environment variants based on the supply-demand curves of the plurality of the products comprises: determining placements of the plurality of products based on the supply-demand curves.
However, Girija discloses wherein generating the plurality of virtual environment variants based on the supply-demand curves of the plurality of the products comprises: determining placements of the plurality of products based on the supply-demand curves (See Girija, at least para. [0047], listing prioritization and personalization engine generates a personalized relevance score for each bookable product and an associated user; para. [0050], listing prioritization and personalization engine 114 may determine a quantitative value for each of the above criteria and may determine the relevance score based on the summation of values of all the criteria to determine the relevance score for each parking lot displayed to the user. The outcome of the listing prioritization and personalization engine 114 along with the business rules determines the products that would be displayed to the user and the display position of each of such products) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the online behavior prediction system and method of Lahav the ability wherein generating the plurality of virtual environment variants based on the supply-demand curves of the plurality of the products comprises: determining placements of the plurality of products based on the supply-demand curves as disclosed by Girija since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so in order to optimize prices of products for a specific customer. (See Girija, at least para. [0003].
Claim 16 is rejected for similar reasons.
Claims 8 and 17: The combination of Lahav and Girija discloses all the limitations of claims 6 and 15 discussed above.
Lahav does not expressly disclose wherein generating the plurality of virtual environment variants based on the supply-demand curves of the plurality of the products comprises: receiving selections of goals of providers of the plurality of products; and determining placements of the plurality of products further based on the goals of the providers.
However, Girija discloses wherein generating the plurality of virtual environment variants based on the supply-demand curves of the plurality of the products comprises:
receiving selections of goals of providers of the plurality of products (See Girija, at least para. [0044], business rules manager 110 may receive various business rules from business admins that are required to determine the final price of the product in accordance with business objectives associated with the e-commerce portal; business rules may include rules such as, but not limited to, pricing rules, supply contracts, revenue share agreements and so on. The business rules manager 110 may determine one or more KPIs based on these business rules and provide the KPIs to other modules of the optimized dynamic pricing engine 100, as illustrated in FIG. 1, to ensure that their outputs are implemented according to the business rules; para. [0045], if a business rule specifies that the target profit margin for a current month is 20%, other modules of the pricing engine may be aligned to this rule. For instance, the listing prioritization and personalization engine 114 may give a higher weightage to products with higher margins in computation of relevance ranking. Consequently, the listing prioritization and personalization engine 114 may set relevance rankings of products in a manner that the margin of products that are likely to be purchased by the user(s) is 20%) ; and
determining placements of the plurality of products further based on the goals of the providers (See Girija, at least para. [0047], listing prioritization and personalization engine generates a personalized relevance score for each bookable product and an associated user; para. [0050], listing prioritization and personalization engine 114 may determine a quantitative value for each of the above criteria and may determine the relevance score based on the summation of values of all the criteria to determine the relevance score for each parking lot displayed to the user. The outcome of the listing prioritization and personalization engine 114 along with the business rules determines the products that would be displayed to the user and the display position of each of such products).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the online behavior prediction system and method of Lahav the ability wherein generating the plurality of virtual environment variants based on the supply-demand curves of the plurality of the products comprises: receiving selections of goals of providers of the plurality of products; and determining placements of the plurality of products further based on the goals of the providers as disclosed by Girija since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so in order to optimize prices of products for a specific customer. (See Girija, at least para. [0003].
Claim 17 is rejected for similar reasons.
Claims 9 and 18: The combination of Lahav and Girija discloses all the limitations of claims 6 and 15 discussed above.
Lahav does not expressly disclose wherein generating the plurality of virtual environment variants based on the supply-demand curves of the plurality of the products comprises: determining whether each of the supply-demand curves of the plurality of products is linear; and determining placements of the plurality of products further based on linearity of the supply-demand curves of the plurality of products.
However, Girija discloses wherein generating the plurality of virtual environment variants based on the supply-demand curves of the plurality of the products comprises:
determining whether each of the supply-demand curves of the plurality of products is linear (See Girija, at least para. [0036], Modelling Engine predicts a supply and demand for all bookable products using known AI & ML algorithms; bookable products may include products for which inventory may be available with a seller/vendor and the user may be interested in purchasing it (e.g. the products being browsed by the user); input includes market data including competitor details such as prices, reviews, and industry data and user profile details from the profile manager module; based on these received values, the AI & ML modelling engine may determine supply and demand predictions; para. [0038], The AI & ML modelling engine 106 may provide the pricing range that is appropriate for meeting business objectives); and
determining placements of the plurality of products further based on linearity of the supply-demand curves of the plurality of products (See Girija, at least para. [0047], listing prioritization and personalization engine generates a personalized relevance score for each bookable product and an associated user; para. [0050], listing prioritization and personalization engine 114 may determine a quantitative value for each of the above criteria and may determine the relevance score based on the summation of values of all the criteria to determine the relevance score for each parking lot displayed to the user. The outcome of the listing prioritization and personalization engine 114 along with the business rules determines the products that would be displayed to the user and the display position of each of such products).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the online behavior prediction system and method of Lahav the ability wherein generating the plurality of virtual environment variants based on the supply-demand curves of the plurality of the products comprises: determining whether each of the supply-demand curves of the plurality of products is linear; and determining placements of the plurality of products further based on linearity of the supply-demand curves of the plurality of products as disclosed by Girija since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so in order to optimize prices of products for a specific customer. (See Girija, at least para. [0003].
Claim 18 is rejected for similar reasons.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNE MARIE GEORGALAS whose telephone number is (571)270-1258 E.S.T.. The examiner can normally be reached on Monday-Friday 8:30am-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached on 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Anne M Georgalas/
Primary Examiner, Art Unit 3689