DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendment filed 04/16/2026.
Claims 1-3, 5-11, and 13-20 have been amended. Claims 1-20 are pending and have been examined on the merits (claims 1, 9, and 17 being independent).
The amendment filed 04/16/2026 to the claims has been entered.
Response to Arguments
Applicant’s arguments and amendments filed 04/16/2026 have been fully considered.
With regard to the rejections of claims 1-20 under 35 U.S.C. 103, Applicant’s arguments and amendments have been considered but are moot as a new ground of rejection has been added and Examiner respectfully disagrees. Examiner notes that Applicant is arguing newly amended claim language. As noted in the citation above the prior art and it is addressed by the rejections under 35 USC 103.
Examiner Notes: The cited reference, Wang has filed on 05/09/2024 and this date is after 07/14/2015. However, the priority date of the amended claims about using “Large Language Model” (LLM) and multiple vectors should be any priority dates after 07/14/2015 and before 10/18/2024 because the provisional application and parent applications do not have the amended claims as described above in the previous claims and specifications.
Applicants assert that the pending claims fully comply with the requirement of 35 U.S.C. 101. Examiner respectfully disagrees. Applicant’s argument and amendments have been considered and are not persuasive. The rejections under 35 U.S.C. 101 have been maintained and clarified in view of the USPTO MPEP 2106.
Applicant’s arguments (see Applicant’s remarks, pages 9-13):
A. Applicant's Claims Are Not Directed to an Abstract Idea
(1) Applicant’s arguments that “Applicant respectfully disagrees. Although the data used by the claimed system may be used in fundamental economic principles or practices and/or commercial or legal interactions, the claimed system is not directed to these principles, practices and/or interactions.” (see page 10), are not found persuasive.
Response (1): Under Step 2 A, Prong 1 of the 2019 Revised § 101 Guidance, it is determined whether the claims are directed to a judicial exception such as a law of nature, a natural phenomenon, or an abstract idea (See Alice, 134 S. Ct. at 2355) by identify the specific limitation(s) in the claim that recites abstract idea(s); and then determine whether the identified limitation(s) falls within at least one of the groupings of abstract ideas enumerated in the MPEP 2106.04. The cited limitations as drafted are systems and methods that, under their broadest reasonable interpretation, covers performance of a method of organizing human activity, but for the recitation of the generic computer components. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of at least one processor. Using transaction data associated with users, merchants, and products in order to make a recommendation is a fundamental economic practice long prevalent in commerce systems. If a claim limitation, under its broadest reasonable interpretation, covers a fundamental economic principle or practice but for the general linking to a technological environment, then it falls within the organizing human activity grouping of abstract ideas. Accordingly, the claim recites an abstract idea..
(2) Applicant’s arguments that “Applicant respectfully disagrees. Although the data used by the claimed system may be used in fundamental economic principles or practices and/or commercial or legal interactions, the claimed system is not directed to these principles, practices and/or interactions.” (see page 10), are not found persuasive.
Response (2): It is determined whether the claim is directed to the abstract concept itself or whether it is instead directed to some technological implementation or application of, or improvement to, this concept, i.e., integrated into a practical application. See, e.g., Alice, 573 U.S. at 223, discussing Diamond v. Diehr, 450 U.S. 175 (1981 ). The mere introduction of a computer or generic computer technology into the claims need not alter the analysis. See Alice, 573 U.S. at 223-24. "[T]he relevant question is whether the claims here do more than simply instruct the practitioner to implement the abstract idea on a generic computer." Alice, 573 U.S. at 225.
In the present case, the judicial exception is not integrated into a practical application. The claim limitations are not indicative of integration into a practical application by claiming an improvement to the functioning of the computer or to any other technology or technical field. Further, the claim limitations are not indicative of integration into a practical application by applying or using the judicial exception in some other meaningful way. In particular the claim limits of “artificial intelligence (AI)”, “database”, “processor”, and “predictive computer models” are claimed and described at a high level of generality and are functions any general purpose computer performs such that it amounts no more than mere instruction to apply the exception to a particular technological environment. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of computer components. The claim limits also recite the use of artificial intelligence (AI), database, processor, and predictive computer models as additional elements. However, the use of these additionally elements, described at a high level of generality, perform generic computer functions such that it amounts to no more than mere instruction to apply the exception to a particular technological environment. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaning limits on practicing the abstract idea. Thus, the claim is directed toward an abstract idea.
B. Applicant's Claims Are Directed to "Significantly More" Than the Abstract Idea
(3) Applicant’s arguments that “Applicant respectfully disagrees. Although the data used by the claimed system may be used in fundamental economic principles or practices and/or commercial or legal interactions, the claimed system is not directed to these principles, practices and/or interactions.” (see page 10), are not found persuasive.
Response (3): The claim(s) does/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 that the abstract idea(s). As discussed above with respect to integration of the abstract idea into a practical application, the additional elements to perform the abstract idea(s) amount to no more than mere instructions to apply an exertion using a computer component. In summary, mere instructions to apply the exertion using a computer cannot provide an inventive concept.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. (2014).
The claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In the instant case, the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
Step (1): In the instant case, the claims are directed towards to a method for merging transaction data associated with users, merchants, and products to generate an output as a recommendation which contains the steps of generating, calculating, and outputting. The claim recites a series of steps and, therefore, is a process. The claims do fall within at least one of the four categories of patent eligible subject matter because claim 1 is direct to a system, claim 9 is direct to a method, and claim 17 is direct to at least one non-transitory computer-readable storage medium, i.e. machines programmed to carrying out process steps, Step 1-yes.
Step (2A) Prong 1: A method for merging transaction data associated with users, merchants, and products to generate an output as a recommendation is akin to the abstract idea subject matter grouping of: Certain Methods of Organizing Human Activity as fundamental economic principles or practices and/ commercial or legal interactions. As such, the claims include an abstract idea.
The specific limitations of the invention are (a) identified to encompass the abstract idea include: { generate a first matrix ….. including first transaction data associated with a first plurality of users, the first matrix correlating a first set of interactions among a first plurality of merchants; generate a second matrix ….. including second transaction data associated with a second plurality of users, the second matrix correlating a second set of interactions among a plurality of products; generate a third matrix including third transaction data associated with a third plurality of users, the third matrix correlating a third set of interactions between products and merchants where the products were purchased; convert, ……, data including text data included in each of the first, second, and third matrices into numerical vector data, wherein converting the data including the text data into the numerical vector data enables combining the converted data with a plurality of preference vectors to generate a plurality of improved predictive …..; generate a preference vector associated with an accountholder of a plurality of accountholders, the preference vector representing historical purchases initiated by the accountholder with a second plurality of merchants; generate an improved predictive …… by combining the numerical vector data with the preference vector for the accountholder; output, ……., a recommendation associated with the, the recommendation including at least one of a merchant or a product predicted for purchasing by the accountholder.}
As stated above, this abstract idea falls into the (b) subject matter grouping of: Certain Methods of Organizing Human Activity as fundamental economic principles or practices and/commercial or legal interactions as merging and using transaction data associated with users, merchants, and products in order to make a recommendation.
Step (2A) Prong 2: The instant claims do not integrate the exception into a practical application because additional elements: 1) “artificial intelligence (AI)”, “database”, “predictive computer models”, and “at least one processor” amount to simply applying the abstract idea to a computer component. (e.g. “apply it”) do not apply, rely on, or use the judicial exception in a manner that that imposes a meaningful limitation on the judicial exception (i.e. generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h) or apply it with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
The instant recited claims including additional elements (i.e. artificial intelligence (AI), database, processor, and predictive computer models) do not improve the functioning of the computer or improve another technology or technical field nor do they recite meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The limitations merely use a generic computing technology (Specification paragraphs [0071-0072]: database server, transaction server, web server, fax server, directory server, mail server, local area network (LAN), workstations, personal computer, Internet) as generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h) or apply it with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)). Therefore, the claims are directed to an abstract idea
Step (2B): The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (Claims: e.g., artificial intelligence (AI), database, processor, and predictive computer models) amount to no more than mere instructions to apply the exactly using generic computer component. The claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea over a generic computer network with a generic computer element.
The computer is merely a platform on which the abstract idea is implemented. Simply executing an abstract concept on a computer does not render a computer “specialized,” nor does it transform a patent-ineligible claim into a patent-eligible one. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1280 (Fed. Cir. 2012). There are no improvements to another technology or technical field, no improvements to the functioning of the computer itself, transformation or reduction of a particular article to a different state or thing or any other meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment as a result of performing the claimed method. Also, the addition of merely novel or non-routine components to the claimed idea does not necessarily turn an abstraction into something concrete (See Ultramercial, Inc. v. Hulu, LLC, _ F.3d_, 2014 WL 5904902, (Fed. Cir. Nov. 14, 2014). Hence, the claims do not recite significantly more than an abstract idea. In conclusion, merely “linking/applying” the exception using generic computer components does not constitute ‘significantly more’ than the abstract idea. (MPEP 2106.05 (f)(h)). Therefore, the claims are not patent eligible under 35 USC 101.
Dependent claims 2-8, 10-16, and 18-20 when analyzed as a whole and in an ordered combination are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as detailed below. The additional recited limitations in the dependent claims only refine the abstract idea.
For instance, in claims 2, 10, and 18, the step of “… further configured to: generate at least one of the first matrix, the second matrix and the third matrix….” (i.e., generating matrix), in claims 3, 11, and 19, the step of “… further configured to train the one or more AI techniques using at least one of the first, second, or third transaction data including merchant data and product data.” (i.e., training AI), in claims 4 and 12, the step of “… include at least one of Recurrent Neural Networks (RNNs),...” (i.e., using RNN), in claims 5 and 13, the step of “… further configured to receive at least one of the first second, or third transaction data...” (i.e., receiving data), in claims 6 and 14, the step of “… further configured to receive additional transaction data...” (i.e., receiving data), in claims 7 and 15, the step of “… wherein the outputted recommendation includes at least one of: (a) an estimate of demand for a new item, (b) recommendations related to implementation of item endcaps in physical stores at a plurality of merchants, (c) loyalty redemption catalogs for at least one the first or second plurality of users ....” (i.e., outputting a recommendation), and in claims 8, 16, and 20, the step of “… (a) determine one or more instant recommendations for in-store items at a store of the one merchant and (b) cause the one or more instant recommendations to be displayed .....” (i.e., provide a recommendation) are all processes that, under its broadest reasonable interpretation, covers performance of a fundamental economic practice but for the recitation of a generic computer component. Using transaction data associated with users, merchants, and products to generate a predictive output as a recommendation is a most fundamental commercial process.
This is an abstract concept with nothing more and is also considered mere instructions to apply an exception akin to a commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd.; Gottschalk and Versata Dev. Group, Inc.; see MPEP 2106.05(f)(2).
In dependent claims 2-8, 10-16, and 18-20, the step claimed are rejected under the same analysis and rationale as the independent claims 1, 9, and 17 above. Merely claiming the same process using transaction data associated with users, merchants, and products to generate a predictive output as a recommendation does not change the abstract idea without an inventive concept or significantly more. Clearly, the additional recited limitations in the dependent claims only refine the abstract idea further. Further refinement of an abstract idea does not convert an abstract idea into something concrete.
Therefore, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
Claims 1, 5-9, 13-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wical, US Publication Number 2014/012228 A1 in view of Rahman et al. (hereinafter Rahman), US Publication Number 2013/0346152 A1 in view of Wang Chunzhu et al. (hereinafter Wang), CN 118152428 A in further view of McGeehan, US Patent Number 8738486 B2.
Regarding claim 1:
Wical discloses the following:
An artificial intelligence (AI)-based prediction recommender system comprising at least one processor and at least one database in communication with the at least one processor, the at least one processor configured to: (see Wical, [0020] discloses “the emergent data processing system can normalize incoming data into a hyper-graph type (or similar) structure that is sufficient to contain these complex relationships, and can then use various artificial intelligence (Al)”, [0021] discloses “The emergent data processing system can be used to provide a deep understanding of any piece of data in the graph, such as a customer, a company's content (e.g., products, offers, deals, supporting collateral), and a competitors related content.”, and see also [0039])
output, from the improved predictive computer model, a recommendation associated with the, the recommendation including at least one of a merchant or a product predicted for purchasing by the accountholder. (see Wical, [0020] discloses “The emergent data processing system 100 can process and correlate the databased on a wide range of factors to generate a relevant recommendation. The resulting recommendation can also be a product but need not be so limited.”)
Wical does not explicitly disclose the following, however Rahman further teaches:
generate a first matrix (reads on “two dimensional matrix 500 that can have a product dimension 404 and a customer dimension 406”) using a large language merchant transaction model including transaction data associated with a first plurality of users, the first matrix correlating a first set of interactions among a first plurality of merchants; (see Rahman, [0044] discloses “The spatial intersection 500 can characterize a two dimensional matrix 500 that can have a product dimension 404 and a customer dimension 406. The particular period of time can be provided by a merchant that can sell the plurality of products to the plurality of customers.”)
generate a second matrix (reads on “two dimensional matrix 500 that can have a product dimension 404 and a customer dimension 406”) using a large language product transaction model including transaction data associated with a second plurality of users, the second matrix correlating a second set of interactions among a plurality of products; (see Rahman, [0044] discloses “The spatial intersection 500 can characterize a two dimensional matrix 500 that can have a product dimension 404 and a customer dimension 406. The particular period of time can be provided by a merchant that can sell the plurality of products to the plurality of customers.”)
generate a third matrix (reads on “three dimensional matrix”) including transaction data associated with a third plurality of users, the third matrix correlating a third set of interactions between products and merchants where the products were purchased; (see Rahman, [0038] discloses “Pairs of products and customers can be randomly obtained at 202 such that the randomly obtained pairs can be located on a spatial intersection of the three dimensional matrix.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the emergent data processing system that normalizes incoming data into a hyper-graph type (or similar) structure that is sufficient to contain these complex relationships, and can then use various artificial intelligence (Al) of Wical to include generating a matrix for pairing of products and customers, as taught by Rahman, in order to provide a recommendation at various groupings of customers and products. (see Rahman, [0001-0002])
Wical and Rahman do not explicitly disclose the following, however Wang further teaches:
convert (reads on “the large language model can convert the input text and other power customer information into semantic vectors and store the semantic vectors into the vector database. By using the large model generation type technology, the input electric power customer information such as texts is converted into semantic vectors”), using at least one large language model, data including text data included in each of the first, second, and third matrices into numerical vector data, wherein converting the data including the text data into the numerical vector data enables combining the converted data with a plurality of preference vectors to generate (reads on “predict the user intention more conveniently, uniformly, rapidly and accurately, and improve the customer service efficiency and customer service satisfaction”) a plurality of improved predictive computer models; (see Wang, page 8, lines 16-20: discloses “The invention relates to a precise prediction and enhancement method and a device for data query intention in an electric power customer service system based on a large model generation technology, which can improve the accuracy of user intention recognition in the electric power customer service system, improve the unified search query efficiency of electric power users, improve the service efficiency, and can understand and predict the user intention more conveniently, uniformly, rapidly and accurately, and improve the customer service efficiency and customer service satisfaction.”; page 9, lines 18-27: discloses “embedding the trained data in the electric power field into a large language model for loading, so as to prepare for converting the text into the semantic vector, and converting the input data text into the semantic vector by adopting the large language model; for subsequent semantic searching using vectors. The vector database is constructed, and the large language model can convert the input text and other power customer information into semantic vectors and store the semantic vectors into the vector database. By using the large model generation type technology, the input electric power customer information such as texts is converted into semantic vectors, and the semantic vectors can capture deep semantic information in the texts, so that people can more accurately understand the real requirements and accurate intentions of query instructions input by electric power customers. And the generated semantic vector is stored in a vector database, so that the subsequent query and use can be facilitated, related vectors can be retrieved from the vector database when needed, and tasks such as similarity calculation, classification and the like are performed, thereby realizing the rapid and accurate processing of the query instruction of the power customer.”)
generate (reads on “predict the user intention more conveniently, uniformly, rapidly and accurately, and improve the customer service efficiency and customer service satisfaction.”) an improved predictive computer model by combining the numerical vector data with the preference vector for the accountholder; and (see Wang, page 8, lines 16-20: discloses “The invention relates to a precise prediction and enhancement method and a device for data query intention in an electric power customer service system based on a large model generation technology, which can improve the accuracy of user intention recognition in the electric power customer service system, improve the unified search query efficiency of electric power users, improve the service efficiency, and can understand and predict the user intention more conveniently, uniformly, rapidly and accurately, and improve the customer service efficiency and customer service satisfaction.”; page 9, lines 18-27: discloses “embedding the trained data in the electric power field into a large language model for loading, so as to prepare for converting the text into the semantic vector, and converting the input data text into the semantic vector by adopting the large language model)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the emergent data processing system that normalizes incoming data into a hyper-graph type (or similar) structure that is sufficient to contain these complex relationships, and can then use various artificial intelligence (Al) of Wical to include using the large language model that can convert the input text and other power customer information into semantic vectors and store the semantic vectors into the vector database and predict the user intention more conveniently, uniformly, rapidly and accurately, and improve the customer service efficiency and customer service satisfaction, as taught by Wang, in order to provide an accurate prediction. (see Wang, pages, 8-9)
Wical, Rahman, and Wang do not explicitly disclose the following, however McGeehan further teaches:
generate a preference vector associated with an accountholder of a plurality of accountholders, the preference vector representing historical purchases initiated by the accountholder with a second plurality of merchants; (see McGeehan, column 16, lines 46-55 discloses “The dot product of the vector is computed, and for this example the dot product is 7.0 (the number of table entries where both A and B have a value of 1).”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the emergent data processing system that normalizes incoming data into a hyper-graph type (or similar) structure that is sufficient to contain these complex relationships, and can then use various artificial intelligence (Al) of Wical to include computing the dot product of the vector, as taught by McGeehan, in order to provide a predictive recommendation. (see McGeehan, C5, L5-49)
Regarding claim 5:
Wical, Rahman, and Wang do not explicitly disclose the following, however McGeehan further teaches:
The AI-based prediction recommender system of Claim 1, wherein the at least one processor is further configured to receive at least one of the first second, or third transaction data from a processing network. (see McGeehan, column 1, lines 55-65, discloses “receiving transaction data from at least one database, predicting a membership of a merchant in a group using at least one prediction algorithm”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the emergent data processing system that normalizes incoming data into a hyper-graph type (or similar) structure that is sufficient to contain these complex relationships, and can then use various artificial intelligence (Al) of Wical to include computing the dot product of the vector, as taught by McGeehan, in order to provide a predictive recommendation. (see McGeehan, C5, L5-49)
Regarding claim 6:
Wical, Rahman, and Wang do not explicitly disclose the following, however McGeehan further teaches:
The AI-based prediction recommender system of Claim 1, wherein the at least one processor is further configured to receive additional transaction data from a processing network, wherein the additional transaction data is associated with additional products. (see McGeehan, column 1, lines 55-65, discloses “receiving transaction data from at least one database, predicting a membership of a merchant in a group using at least one prediction algorithm”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the emergent data processing system that normalizes incoming data into a hyper-graph type (or similar) structure that is sufficient to contain these complex relationships, and can then use various artificial intelligence (Al) of Wical to include computing the dot product of the vector, as taught by McGeehan, in order to provide a predictive recommendation. (see McGeehan, C5, L5-49)
Regarding claim 7:
Wical does not explicitly disclose the following, however Rahman further teaches:
The AI-based prediction recommender system of Claim 1, wherein the outputted recommendation includes at least one of: (a) an estimate of demand for a new item, (b) recommendations related to implementation of item endcaps in physical stores at a plurality of merchants, (c) loyalty redemption catalogs for at least one the first or second plurality of users, (d) enhanced, personalized online shopping recommendations for at least one the first or second plurality of users, or (e) instant recommendations for in-store items at a store of one of the plurality of merchants. (see Rahman, [0021] discloses “the provided offers by those other customers is increased. Such an increased likelihood of use of offers can cause an increase in sales, profit, and loyalty associated with the products for which offers are provided to those other customers.”, and notes: the recited claim requires only at least one of items a through e)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the emergent data processing system that normalizes incoming data into a hyper-graph type (or similar) structure that is sufficient to contain these complex relationships, and can then use various artificial intelligence (Al) of Wical to include generating a matrix for pairing of products and customers, as taught by Rahman, in order to provide a recommendation at various groupings of customers and products. (see Rahman, [0001-0002])
Regarding claim 8:
Wical discloses the following:
The AI-based prediction recommender system of Claim 1, wherein the at least one processor is further configured to interface with a computer application associated with one of the plurality of merchant to: (a) determine one or more instant recommendations for in-store items at a store of the merchant and (b) cause the one or more instant recommendations to be displayed, via the computer application, on a user computing device of one of the first or second plurality of users. (see Wical, [0048] discloses “the emergent data processing system 100 can evaluate all of the recommendations against information present in the individual’s Social network, which may intersect in various ways to the items being recommended”)
Regarding claims 9 and 17: it is similar scope to claim 1, and thus it is rejected under similar rationale.
Regarding claim 13: it is similar scope to claim 5, and thus it is rejected under similar rationale.
Regarding claim 14: it is similar scope to claim 6, and thus it is rejected under similar rationale.
Regarding claim 15: it is similar scope to claim 7, and thus it is rejected under similar rationale.
Regarding claims 16 and 20: it is similar scope to claim 8, and thus it is rejected under similar rationale.
Claims 2-3, 10-11, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wical in view of Rahman in view of Wang in view of McGeehan in further view of Yemini et al. (hereinafter Yemini), US Patent Number 6249755 B1.
Regarding claim 2:
Wical, Rahman, Wang, and McGeehan do not explicitly disclose the following, however Yemini further teaches:
The AI-based prediction recommender system of Claim 1, wherein the at least one processor is further configured to: generate at least one of the first matrix, the second matrix and the third matrix using one or more AI techniques. (see Yemini, column 24, lines 4-35, discloses “Real-time correlation computations are reduced significantly by preprocessing event knowledge to generate codebooks prior to real-time event detection and correlation. This is in contrast to typical event correlation systems based on artificial intelligence techniques which conduct indefinite searches during real time to correlate events…. Thus, event capture 7 and event validation 8 shown in FIG. 1A may be used to generate causality matrix 9”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the emergent data processing system that normalizes incoming data into a hyper-graph type (or similar) structure that is sufficient to contain these complex relationships, and can then use various artificial intelligence (Al) of Wical to include generating causality matrix based on artificial intelligence techniques, as taught by Yemini, in order to provide a mapping groups of events. (see Yemini, C14, L14-64)
Regarding claim 3:
Wical, Rahman, Wang, and McGeehan do not explicitly disclose the following, however Yemini further teaches:
The AI-based prediction recommender system of Claim 2, wherein the at least one processor is further configured to train the one or more AI techniques using at least one of the first, second, or third transaction data including merchant data and product data. (see Yemini, column 24, lines 4-35, discloses “Real-time correlation computations are reduced significantly by preprocessing event knowledge to generate codebooks prior to real-time event detection and correlation. This is in contrast to typical event correlation systems based on artificial intelligence techniques which conduct indefinite searches during real time to correlate events”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the emergent data processing system that normalizes incoming data into a hyper-graph type (or similar) structure that is sufficient to contain these complex relationships, and can then use various artificial intelligence (Al) of Wical to include generating causality matrix based on artificial intelligence techniques, as taught by Yemini, in order to provide a mapping groups of events. (see Yemini, C14, L14-64)
Regarding claims 10 and 18: it is similar scope to claim 2, and thus it is rejected under similar rationale.
Regarding claims 11 and 19: it is similar scope to claim 3, and thus it is rejected under similar rationale.
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Wical in view of Rahman in view of Wang in view of McGeehan in view of Yemini in further view of Wilson et al. (hereinafter Wilson), US Patent Number 9009088 B2.
Regarding claim 4:
Wical, Rahman, Wang, McGeehan, and Yemini do not explicitly disclose the following, however Wilson further teaches:
The AI-based prediction recommender system of Claim 2, wherein the one or more AI techniques include at least one of Recurrent Neural Networks (RNNs), Generative AI, or PAGERANK®. (see Wilson, column 12, lines 1-3, discloses “The neural network may be refined based on an active feedback loop concerning the effectiveness of the recommendations provided by the system 100.”)
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the emergent data processing system that normalizes incoming data into a hyper-graph type (or similar) structure that is sufficient to contain these complex relationships, and can then use various artificial intelligence (Al) of Wical to include the neural network, as taught by Wilson, in order to provide a better recommendation. (see Wilson, C1, L20-44)
Regarding claim 12: it is similar scope to claim 4, and thus it is rejected under similar rationale.
Conclusion
The prior art made of record but not relied upon herein but pertinent to Applicant’s disclosure is listed in the enclosed PTO-892.
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 YONG S PARK whose telephone number is (571)272-8349. The examiner can normally be reached on M-F 9:00-5:00 PM, EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bennett M. Sigmond can be reached on (303)297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/YONGSIK PARK/Examiner, Art Unit 3694 June 12, 2026
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694
Prosecution Insights