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 .
DETAILED ACTION
1. The following is a non-final, First Office Action on the merits. Claims 1-20 are pending.
The Examiner’s Note:
2. Independent claims 1, 12 and 18 as a whole recites a combination of limitations that has been found as defined over prior art of record {The combination of Shariff et al; (US 2019/0012682 A1), Mead et al; (US 2022/0284463 A1), Perkins et al; (US 2013/0073352 A1), and Ettl et al; (US 2017/0278173 A1) fails to fairly teach such a system and method, as claimed as a whole, including the underlined subject matter: “determining a first total discounted future reward for the first ordered list of items based on a reward discount parameter, the item having a discounted future reward value determined based on the reward value of the item, an associated position of the item in the first ordered list of items, and the reward discount parameter; determining a first risk estimate for the first ordered list of items based on a risk discount parameter, the item having a discounted risk estimate determined based on the relevance score of the item, the associated position of the item in the first ordered list of items, and the risk discount parameter; selecting a second ordered list of items from the plurality of recommended items in the content platform for the user considering both the reward discount parameter and the risk discount parameter, wherein the second ordered list of items has a second total discounted future reward determined based on the reward discount parameter and a second risk estimate determined based on the risk discount parameter, wherein the second total discounted future reward is larger than or equal to the first total discounted future reward, and the second risk estimate is less than or equal to the first risk estimate”.
However, please note that claims 1-20 are rejected under 101 and double patenting below.
Double Patenting
3. Independent claims 1, 12 and 18 of the instant application are rejected on the ground of nonstatutory double patenting as being unpatentable respectively over claims 1, 12 and 17 of US Patent 12,561,711 B2. Further, dependent claims 2, 13 and 19 of the instant application are rejected on the ground of nonstatutory double patenting as being unpatentable respectively over claims 1, 12 and 17 of US Patent 12,561,711 B2. In addition, dependent claims {3}; {4}; {5,14}; {6,15,20}; {7,6}; {8,7}; {9,16}; {10,17}; and 11 of the instant application are rejected on the ground of nonstatutory double patenting as being unpatentable respectively over dependent claims {2}; {3}; {4,1318}; {5,14,19}; {6}; {7}; {8,15}; {9,16}; and 11 of U.S. Patent 12,561,711 B2. See table below.
Instant Application: 19/456,772
US Patent 12,561,711 B2
Claims 1, 12 and 18: A computer implemented method for generating recommended items, the computer-implemented method comprising:
determining, by at least one computer processor, using a model representing a distributed representation of words in a corpus, a relevance score of an item of a plurality of items in a content platform based on information about a user account of a user, the relevance score of the item representing a probability the item is going to be consumed by the user based on a query from a user device;
determining a reward value of the item based on the information about the user account; selecting a first ordered list of items from the plurality of items in the content platform based on the relevance score for each item in the content platform;
determining a first total discounted future reward for the first ordered list of items based on a reward discount parameter, the item having a discounted future reward value determined based on the reward value of the item, an associated position of the item in the first ordered list of items, and the reward discount parameter;
determining a first risk estimate for the first ordered list of items based on a risk discount parameter, the item having a discounted risk estimate determined based on the relevance score of the item, the associated position of the item in the first ordered list of items, and the risk discount parameter; and
selecting a second ordered list of items from the plurality of items as an ordered list of recommended items in the content platform for the user considering both the reward discount parameter and the risk discount parameter, wherein the second ordered list of items has a second total discounted future reward determined based on the reward discount parameter and a second risk estimate determined based on the risk discount parameter, wherein the second total discounted future reward is larger than or equal to the first total discounted future reward, and the second risk estimate is less than or equal to the first risk estimate.
Claims 2, 13 and 19. The computer-implemented method of claim 1, further comprising: receiving the query from the user device for items in the content platform that are relevant to the user operating the user device; and providing the ordered list of recommended items to the user device.
Claim 3. The computer-implemented method of claim 1, wherein the reward value of the item indicates a monetary value generated in response to the item being consumed by the user.
Claim 4. The computer-implemented method of claim 1, further comprising: automatically turning on or off closed caption of one or more items of the ordered list of recommended items during streaming of the one or more items by the user device.
Claims 5 and 14. The computer-implemented method of claim 1, wherein the second ordered list of items is located at an efficient frontier including multiple ordered lists obtained by permutations of the first ordered list of items, an ordered list of the efficient frontier having a total discounted future reward and a risk estimate determined based on the reward discount parameter, the risk discount parameter, the relevance score of the item, or the reward value of the item.
Claims 6, 15 and 20. The computer-implemented method of claim 5, wherein there is no ordered list of items obtained by a permutation of the first ordered list of items having a total discounted future reward bigger than the second total discounted future reward and a risk estimate smaller than the second risk estimate.
Claim 7. The computer-implemented method of claim 1, wherein the plurality of items are selected from a set of items including at least an additional item not included in the plurality of items, the relevance score of the item is larger than or equal to a relevance score of the additional item.
Claim 8. The computer-implemented method of claim 7, wherein the item of the plurality of items and the additional item of the set of items include media content to be provided by the content platform to the user.
Claims 9 and 16. The computer-implemented method of claim 1, wherein the first ordered list of items includes {Item1, Item2, . . . ItemK} having the first ordered list of reward values {Rev1, Rev2, . . . RevK}, and the first total discounted future reward is determined by a formula Rev1+Rev2*γ+ . . . αRevK*γK−1, where K is a total number of items in the first ordered list of items, and γ is the reward discount parameter.
Claims 10 and 17. The computer-implemented method of claim 1, wherein the first ordered list of items includes {Item1, Item2, . . . ItemK} having the first ordered list of relevance scores {Rel1, Rel2, . . . RelK}, and the first risk estimate is determined based on a formula β*(1−Rel1)2+β2*(1−Rel2)2+ . . . +γK*(1−RelK)2, where K is a total number of items in the first ordered list of items, and β is the risk discount parameter.
Claim 11. The computer-implemented method of claim 1, wherein the reward discount parameter or the risk discount parameter is determined based on a number of user click event, a number of user launch event, a number of user focus event, or a number of user scrolling event stored in the information about the user account of the user.
Claims 1, 12 and 17: A computer-implemented method for a search system to perform an improved search process to generate recommended items, the computer-implemented method comprising:
receiving, by the search system operated by at least one computer processor of a content server of a content platform, a query from a user device for items in the content platform that are relevant to a user operating the user device;
determining, by the at least one computer processor, a first ordered list of items from a plurality of items in the content platform based on the query and a user account of the user, wherein the determining comprises: determining, using a model representing a distributed representation of words in a corpus, a relevance score of an item in the content platform based on information about the user account, the relevance score of the item representing a probability the item is going to be consumed by the user based on the query;
determining a reward value of the item based on the information about the user account; and selecting the first ordered list of items from the plurality of items in the content platform based on the relevance score for each item in the content platform;
determining a first total discounted future reward for the first ordered list of items based on a reward discount parameter, the item having a discounted future reward value determined based on the reward value of the item, an associated position of the item in the first ordered list of items, and the reward discount parameter;
determining a first risk estimate for the first ordered list of items based on a risk discount parameter, the item having a discounted risk estimate determined based on the relevance score of the item, the associated position of the item in the first ordered list of items, and the risk discount parameter;
selecting a second ordered list of items from the plurality of items as an ordered list of recommended items in the content platform for the user considering both the reward discount parameter and the risk discount parameter, wherein the second ordered list of items has a second total discounted future reward determined based on the reward discount parameter and a second risk estimate determined based on the risk discount parameter, wherein the second total discounted future reward is larger than or equal to the first total discounted future reward, and the second risk estimate is less than or equal to the first risk estimate; and providing the ordered list of recommended items to the user device.
Is taught by the bold limitations in claims 1, 12 and 17
Claim 2. The computer-implemented method of claim 1, wherein the reward value of the item indicates a monetary value generated in response to the item being consumed by the user.
Claim 3. The computer-implemented method of claim 1, further comprising: automatically turning on or off closed caption of one or more items of the ordered list of recommended items during streaming of the one or more items by the user device.
Claims 4, 13 and 18. The computer-implemented method of claim 1, wherein the second ordered list of items is located at an efficient frontier including multiple ordered lists obtained by permutations of the first ordered list of items, an ordered list of the efficient frontier having a total discounted future reward and a risk estimate determined based on the reward discount parameter, the risk discount parameter, the relevance score of the item, or the reward value of the item.
Claims 5, 14 and 19. The computer-implemented method of claim 4, wherein there is no ordered list of items obtained by a permutation of the first ordered list of items having a total discounted future reward bigger than the second total discounted future reward and a risk estimate smaller than the second risk estimate.
Claim 6. The computer-implemented method of claim 1, wherein the plurality of items are selected from a set of items including at least an additional item not included in the plurality of items, and the relevance score of the item is larger than or equal to a relevance score of the additional item.
Claim 7. The computer-implemented method of claim 6, wherein the item of the plurality of items and the additional item of the set of items include media content to be provided by the content platform to the user.
Claims 8 and 15. The computer-implemented method of claim 1, wherein the first ordered list of items includes {Item.sub.1, Item.sub.2, . . . Item.sub.K} having the first ordered list of reward values {Rev.sub.1, Rev.sub.2, . . . Rev.sub.K}, and the first total discounted future reward is determined by a formula Rev.sub.1+Rev.sub.2*γ+ . . . +Rev.sub.K*γ.sup.K-1, where K is a total number of items in the first ordered list of items, and γ is the reward discount parameter.
Claims 9 and 16. The computer-implemented method of claim 1, wherein the first ordered list of items includes {Item.sub.1, Item.sub.2, . . . Item.sub.K} having the first ordered list of relevance scores {Rel.sub.1, Rel.sub.2, . . . Rel.sub.K}, and the first risk estimate is determined based on a formula β*(1−Rel.sub.1).sup.2+β.sup.2*(1−Rel.sub.2).sup.2+ . . . +β.sup.K*(1−Rel.sub.K).sup.2, where K is a total number of items in the first ordered list of items, and β is the risk discount parameter.
Claim 11. The computer-implemented method of claim 1, wherein the reward discount parameter or the risk discount parameter is determined based on a number of user click events, a number of user launch events, a number of user focus events, or a number of user scrolling events stored in the information about the user account of the user.
In summary, independent claims 1, 12 and 17 of U.S. Patent 12,561,711 B2 teach all the features of independent claims 1, 12 and 18 of the instant application as indicated in the table above. Although claims 1, 12 and 17 of U.S. Patent 12,561,711 B2 has additional underlined features (bold limitations in the table above) such as {e.g., receiving, by the search system operated by at least one computer processor of a content server of a content platform, a query from a user device for items in the content platform that are relevant to a user operating the user device; determining, by the at least one computer processor, a first ordered list of items from a plurality of items in the content platform based on the query and a user account of the user, wherein the determining comprises:…..; and providing the ordered list of recommended items to the user device.}; however, it has been held that deleting elements when the function of element is not desired is obvious. See MPEP 2144.04 Section II. Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify or to omit the additional elements of 1, 12 and 17 of U.S. Patent 12,561,711 B2 the to arrive at the claims 1, 12 and 18 of the instant application because the person of ordinary skill would have realized that the remaining element would perform the same functions as before. “Omission of element and its function in combination is obvious expedient if the remaining elements perform same functions as before.” See In re Karlson (CCPA) 136 USPQ 184, decide Jan 16, 1963, Appl. No. 6857, U. S. Court of Customs and Patent Appeals.
Further, bold limitations in independent claims 1, 12 and 17 of U.S. Patent 12,561,711 B2 also teach dependent claims 2, 13 and 19 of the instant application. In addition, dependent claims {2}; {3}; {4,1318}; {5,14,19}; {6}; {7}; {8,15}; {9,16}; and 11 of U.S. Patent 12,561,711 B2 respectively teach all the features of dependent claims {3}; {4}; {5,14}; {6,15,20}; {7,6}; {8,7}; {9,16}; {10,17}; and 11 of the instant application as indicated in the table above.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
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.
4. The claimed invention (Claims 1-20) is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) abstract ideas including “Certain Methods of Organizing Human Activity”; and/or “Mathematical concepts”, which has/have been identified/found by the courts as abstract ideas in MPEP 2106.04(a). This judicial exception is not integrated into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because it/they is/are recited at a high level of generality and/or are recited as performing generic computer functions routinely used in the computer applications:
5. Step 1: Does the Claim Fall within a statutory Category?
Claims 1-11: Yes, these claims are method, and therefore are directed to the statutory class of process.
Claims 12-17: Yes, these claims are system, which recites at least one processor each coupled to at least one of the memories and configured to perform….., and therefore are directed to the statutory class of machine and article of manufacture.
Claims 18-20: Yes, the claims recite a non-transitory computer-readable medium, which is interpreted as a system because it recites a computer to perform…., and therefore are directed to the statutory class of machine.
6. Step 2A prong 1, Step 2A prong 2 and Step 2B:
Independent claim 12 (Step 2A, Prong I): is directed to an abstract idea of “Certain Methods of Organizing Human Activity”, and/or “Mathematical concepts”:
Limitations 1-6 of determining using a distributed representation of words in a corpus,
a relevance score of an item of a plurality of items based on information about a user account of a user, the relevance score of the item representing a probability the item is going to be consumed by the user based on a query from a user (limitation 1); determining a reward value of the item based on the information about the user account (limitation 2); selecting a first ordered list of items from the plurality of items in the content platform based on the determined relevance score for each item (limitation 3); determining a first total discounted future reward for the first ordered list of items based on a reward discount parameter, the item having a discounted future reward value determined based on the reward value of the item, an associated position of the item in the first ordered list of items, and the reward discount parameter (limitation 4); determining a first risk estimate for the first ordered list of items based on a risk discount parameter, the item having a discounted risk estimate determined based on the relevance score of the item, the associated position of the item in the first ordered list of items, and the risk discount parameter (limitation 5); and selecting a second ordered list of items from the plurality of items as an ordered list of recommended items in the content platform for the user considering both the reward discount parameter and the risk discount parameter, wherein the second ordered list of items has a second total discounted future reward determined based on the reward discount parameter and a second risk estimate determined based on the risk discount parameter, wherein the second total discounted future reward is larger than or equal to the first total discounted future reward, and the second risk estimate is less than or equal to the first risk estimate (limitation 6) fall within “Certain Methods of Organizing Human Activity” grouping of abstract idea because these steps mainly describe the concepts of commercial or legal interactions (include subject matter relating to agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and/or managing personal behavior or relationships or interactions between people (including following rules or instructions).
In addition, the steps 1-2, and 4-6 mentioned above of determining using a distributed representation of words in a corpus, a relevance score of an item of a plurality of items based on information about a user account of a user, the relevance score of the item representing a probability the item is going to be consumed by the user based on a query from a user (limitation 1); determining a reward value of the item based on the information about the user account (limitation 2); determining a first total discounted future reward for the first ordered list of items based on a reward discount parameter, the item having a discounted future reward value determined based on the reward value of the item, an associated position of the item in the first ordered list of items, and the reward discount parameter (limitation 4); determining a first risk estimate for the first ordered list of items based on a risk discount parameter, the item having a discounted risk estimate determined based on the relevance score of the item, the associated position of the item in the first ordered list of items, and the risk discount parameter (limitation 5); and selecting a second ordered list of items from the plurality of items as an ordered list of recommended items in the content platform for the user considering both the reward discount parameter and the risk discount parameter, wherein the second ordered list of items has a second total discounted future reward determined based on the reward discount parameter and a second risk estimate determined based on the risk discount parameter, wherein the second total discounted future reward is larger than or equal to the first total discounted future reward, and the second risk estimate is less than or equal to the first risk estimate (limitation 6) also falls within “Mathematical concepts” (mathematical relationships, mathematical formulas or equations, mathematical calculations) grouping of abstract idea because there are mathematical operations/formulas involve in order to determine/calculate a relevance score, a reward value…., a first total discounted future reward, a first risk estimate for the first ordered list of items, a discounted risk estimate, a second total discounted future reward, a second risk estimate….etc., as reflected in Applicant’s specification Publication 2026/0154704 A1 in paragraphs 0050-0067, and claims 9-10, 16-17. See MPEP 2106.04(a)(2).
Independent claim 12, Step 2A (Prong II): Accordingly, the claim recites an abstract idea(s) as pointed out above. This judicial exception(s) is/are not integrated into a practical application. In particular, the claim recites additional underlined elements (i.e., one or more memories; at least one processor each coupled to at least one or the one or more memories and configured to perform operations….; a model; a content platform/the content platform….) to perform abstract steps/limitations 1-6 mentioned above. The additional element(s) in all of the steps is/are -recited at a high-level of generality such that it amounts no more than mere instructions to apply the judicial exception(s) using a generic computer component(s) (i.e., one or more memories; at least one processor each coupled to at least one or the one or more memories and configured to perform operations….; a model; a content platform/the content platform ….); thus, they do not integrate the identified abstract idea into a practical application. See MPEP 2106.05(f). Further, in claim 12, the limitation “one or more memories configured to store a reward discount parameter, a risk discount parameter, a set of items, and at least a user account of a user” is merely storing data, which is considered as “insignificant extra solution activity”; thus, it does not integrate the identified abstract idea into a practical application. See MPEP 2106.05(g). In addition, the additional element of “a user device” mentioned in limitation 1, is merely recited as a source, where information (e.g., a query) is being received from, which is considered as general link to technological environment; thus, it does not integrate the identified abstract idea into a practical application. See MPEP 2106.05(h). Accordingly, again, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Again, the claim is directed to an abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, again, the additional element of using generic computer components (i.e., one or more memories; at least one processor each coupled to at least one or the one or more memories and configured to perform operations….; a model; a content platform/the content platform ….) to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. see MPEP 2106.05(f). For the above mentioned reasons, viewed the claim as a whole, the additional elements/additional steps/additional limitations individually and in combination do not integrate the identified abstract idea into a practical application. Furthermore, there is neither improvement to another technology or technical field nor an improvement to the functioning of the computer itself.
Independent claim 12 (step 2B): The additional element in claim 12 (i.e., one or more memories; at least one processor each coupled to at least one or the one or more memories and configured to perform operations….; a model; a content platform/the content platform …..) is/are recited at a high level of generality and/or are recited as performing generic computer functions routinely used in the computer applications; thus they are not significantly more than the identified abstract idea. In other word, the underlined additional elements “i.e., one or more memories; at least one processor each coupled to at least one or the one or more memories and configured to perform operations….; a model; a content platform/the content platform ….….” is/are amounts no more than mere instructions to apply the judicial exception(s) of steps/limitations 1-6 mentioned above; thus, they are not significantly more than the identified abstract idea. see MPEP 2106.05(f). Further, in claim 12, the limitation “one or more memories configured to store a reward discount parameter, a risk discount parameter, a set of items, and at least a user account of a user” is merely storing data, which is considered as “insignificant extra solution activity”; thus, is not significantly more than the identified abstract idea. See MPEP 2106.05(g). In addition, the additional element of “a user device” mentioned in limitation 1, is merely recited as a source, where information (e.g., a query) is being received from, which is considered as general link to technological environment; thus, is not significantly more than the identified abstract idea. See MPEP 2106.05(h). Again, the claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
When revaluating mentioned above of “one or more memories configured to store a reward discount parameter, a risk discount parameter, a set of items, and at least a user account of a user” in step 2B here, this storing data limitation is also well-understood, routine and conventional activities. The use of generic computer to receive data/gather data and store data through an unspecified generic computer does not impose any meaningful limit on the computer implementation of the abstract idea, and is/are considered as well-understood, routine, conventional activity. According to MPEP 2106.05 (d), elements that the Courts have recognized as well-understood, routine, conventional activity in particular fields are e.g., "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); 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); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93”.
Thus, evidences has been provided to show these additional elements are well-understood, routine, conventional activity according to MPEP 2106.07 (a) (III). Therefore, for the above mentioned reasons, viewed as a whole, even in combination, the above additional steps/additional elements/additional limitations do not amount to significantly more/do not provide an inventive concept. Furthermore, there is neither improvement to another technology or technical field nor an improvement to the functioning of the computer itself.
As per independent claims 1 and claim 18: Alice Corp. also establishes that the same analysis should be used for all categories of claims. Therefore, a method claim 1 and a non-transitory computer-readable medium claim 18 are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same/similar reasons as the system claim(s) 16. The underlined components (i.e., at least one computer processor; a model; a content platform/the content platform; a user device; a non-transitory computer-readable medium having instructions stored…., when executed by a at least a computing device….) described in independent claims 1 and/or 18 add nothing of substance to the underlying abstract idea. They are merely using as tools to implement the identified abstract idea and/or are general link to technological environment. Thus, are not significantly more than the identified abstract idea. At best, the claim(s) are merely providing an environment to implement the abstract idea.
Dependent claims 2-11, 13-17 and 19-20 are merely add further details of the abstract steps/elements recited in claims 1, 12 and 18 without including an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Note that in dependent claims 2, 13 and 19, the limitations “receiving the query from the user device…..; and “providing the ordered list of recommended items to the user device….” are merely receiving data, and transmitting data/displaying data/sending data, which are considered as “insignificant extra solution activity”; thus, do not integrate the identified abstract idea into a practical application, and are not significantly more than the identified abstract idea. See MPEP 2106.05(g). Also, in dependent claim 4, the additional element “automatically…” is recited at a high level of generality and/or are recited as performing generic computer functions routinely used in the computer applications; thus, it does not integrate the identified abstract idea into a practical application, and is not significantly more than the identified abstract idea.. see MPEP 2106.05(f).
Therefore, looking at the limitations as an ordered combinations adds nothing that is not already present when looking at the elements taken individually. Furthermore, there is neither improvement to another technology or technical field nor an improvement to the functioning of the computer itself. Therefore, dependent claims 2-11, 13-17 and 19-20 are also non-statutory subject matter.
Prior Art that is pertinent to Applicant’s disclosure
7. The prior art made of record is considered pertinent to applicant's disclosure.
Shariff et al; (US 2019/0012682 A1), wherein teaches Provided herein are systems, methods and computer readable media for managing a sales pipeline, and in some embodiments, generating demand based on real time demand and predicted demand. An example method comprises generating a virtual promotion, wherein the virtual promotion comprises a combination of a category or sub-category, a location, and a price range, calculating a probability that a particular consumer would buy the virtual offer in a predetermined time period, wherein the probability is generated at least based on historical data related to the particular consumer and one or more related consumers, determining an estimated number of units to be sold for the virtual offer as a function of at least the probability, the estimated number of units representing a predicted demand, calculating a real time demand, wherein the real time demand is generated based on a plurality of generated identification pairs for the predetermined time period, and determining, using a processor, total demand by summing the predicted demand and the real time demand.
Mead et al; (US 2022/0284463 A1), wherein teaches A system and method for operating a bundle-type checkout system incorporating subscription tiers, comprising an offer manager that receives price offer values from consumers, a discount optimization manager that calculates a least acceptable offer value based on offer pattern data associated to each product identifier of the bundle, calculates a total available discount for the bundle based on the least acceptable offer value and retail prices of each product identifier of the bundle, calculates price discount thresholds and compares price discount values to offer values to calculate price discounts and determines acceptability of the price offer values, and weighted discount distribution among subscription tiers to calculate a final price for each product identifier.
Perkins et al; (US 2013/0073352 A1), wherein teaches A system and method for sellers to provide dynamic group discounting to buyers of products and/or services is provided. The system and method allows a seller to determine discount parameters that will be applied to a quantity of goods and/or services to be sold. A purchaser agrees to pay full price for the goods and/or services and may later be provided with a unique discount relative to other purchasers based in part on when the purchaser bought the goods and/or services during the sale period. The system and method may utilize a discount curve to determine the amount of the discount each individual purchaser, or groups of purchasers, will receive.
Ettl et al; (US 2017/0278173 A1), wherein teaches An aspect of the disclosure includes a method, a system and a computer program product for determining a personalized bundle offer for a consumer. The method includes determining an interest in an initial product by a consumer. A demand group is identified based on the initial product. A purchase probability is determined for the consumer to purchase the product. An inventory expected profit-to-go is determined for the product. At least one additional product from the demand group is determined based at least in part on the purchase probability and the inventory expected profit-to-go, the expected profit to go being based on a current inventory state of the product and the at least one additional product. A signal is transmitted to the consumer, the signal including at least one additional product and a price for a bundle containing both the product of interest and the at least one additional product.
Gause; (US 2024/0290171 A1), wherein teaches An electronic sports wagering exchange system to facilitating a wager-based transaction for a plurality of products or services. The system receives a wager from a user for a predetermined event through a sports betting module. The system maintains a pricing data and an availability data for each of the plurality of products or services using an inventory tracking module. The system receives a purchase order for one or more products or services from the plurality of products or services via an order processing module. The system adjusts a purchase price through the order processing module for the purchase order based on an outcome of the wager. The system dynamically determines a value of a discount or coupon through an odds calculation module based on a plurality of evolving betting odds and a potential payout of the wager.
Ouimet; (US 2014/0344051 A1), wherein teaches A commerce system has retailers offering products for sale to consumers. A consideration set including a plurality of products is provided. An interface is provided to submit a profit share corresponding to each of the products. The products are evaluated to determine a discount for each of the products. A first product is selected from the products including a largest profit share. The commerce system is controlled by distributing the largest profit share, an incremental profit corresponding to the first product, and a shopping list including the first product. A value of each of the products is determined to identify a maximum value. The discount is determined for each of the products to increase the value of each of the products to the maximum value. The set of products is accessed to provide a price per use for each of the products.
Gaurav et al; (US 2021/0374799 A1), wherein teaches Various methods are provided for intelligently modeling a diverse promotion portfolio. One example method may comprise utilizing an analytical model to intelligently generate a proposed promotion portfolio of available promotions by performing an iterative process in which a determination is made as to whether the proposed promotion portfolio that is generated projects a predicted revenue that at least meets the target revenue over the predefined period of time for the geographic area, in an instance in which the determined proposed promotion portfolio value fails to meet the target revenue over the predefined period of time for the geographic area, repeating the iterative process and altering the weighting values, in an instance in which the determined proposed promotion portfolio value at least meets the target revenue over the predefined period of time for the geographic area, ending iterative process, and generating the inventory of promotions in accordance with the proposed promotion portfolio.
Further see other reference in PTO-892 form.
Conclusion
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/THUY N NGUYEN/
Primary Examiner, Art Unit 3622.
