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 . 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.
Status of the Claims
The pending claims in the present application are claims 1-20, as presented in the Response filed on 13 January 2026.
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 an abstract idea without significantly more. The paragraphs below provide rationales for the rejection. The rationales are based on the multi-step subject matter eligibility test outlined in MPEP 2106.
Step 1 of the eligibility analysis involves determining whether a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101. (See MPEP 2106.03(I).) That is, Step 1 asks whether a claim is to a process, machine, manufacture, or composition of matter. (See MPEP 2106.03(II).) The “system” of claims 1-10 constitutes a machine under 35 USC 101, and the “method” of claims 11-20 constitutes a process under the statute. Accordingly, claims 1-20 meet the criteria of Step 1 of the eligibility analysis. The claims, however, fail to meet the criteria of subsequent steps of the eligibility analysis, as explained in the paragraphs below.
The next step of the eligibility analysis, Step 2A, involves determining whether a claim is directed to a judicial exception. (See MPEP 2106.04(II).) This step asks whether a claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. (See id.) Step 2A is a two-prong inquiry. (See MPEP 2106.04(II)(A).) Prong One and Prong Two are addressed below.
In the context of Step 2A of the eligibility analysis, Prong One asks whether a claim recites an abstract idea, law of nature, or natural phenomenon. (See MPEP 2106.04(II)(A)(1).) Using claim 1 as an example, the claim recites the following abstract idea limitations:
“... perform operations comprising: receiving ... first transaction data of first transactions for first items; ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... determining, based on the first transaction data, first micro-intents associated with the first transactions; ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... determining one or more second micro-intents associated with second transactions; ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... receiving current transaction data of a user transaction ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... determining, based on the current transaction data, one or more third micro-intents associated with the user transaction; ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... determining that the user is expressing a current micro-intent based on the first micro-intents, the one or more second micro-intents, and the one or more third micro-intents; and ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... display a ... element ..., wherein the ... element correlated with at least one of the first micro-intents, the one or more second micro-intents, or the one or more third micro-intents.” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
The above-listed limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, fall under enumerated groupings of abstract ideas outlined in MPEP 2106.04(a). For example, limitations of the claim can be characterized as: commercial interactions, including advertising, marketing, or sales activities or behaviors involving customer intent, which fall under the certain methods of organizing human activity grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claim also can be characterized as: concepts performed in the human mind, including observation (e.g., the recited “receiving” and “display” steps), and evaluation, judgment, and/or opinion (e.g., the recited “determining” steps), which fall under the mental processes grouping of abstract ideas (see MPEP 2106.04(a)). Accordingly, for at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong One of the eligibility analysis.
In the context of Step 2A of the eligibility analysis, Prong Two asks if the claim recites additional elements that integrate the judicial exception into a practical application. (See MPEP 2106.04(II)(A)(2).) Continuing to use claim 1 as an example, the claim recites the following additional element limitations:
The claimed “perform” is carried out by “A system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “receiving” is performed “using a distributed network comprising a distributed memory architecture to reduce congestion while allowing data access” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “user transaction” is “from a user interface of an electronic device of a user during a current browsing session of a website” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “display” stems from “transmitting an instruction” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “element” is a “user interface element on the user interface of the electronic device” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The above-listed additional element limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, are analogous to: accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, mere automation of manual processes, instructions to display two sets of information on a computer display in a non-interfering manner, without any limitations specifying how to achieve the desired result, and arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, which courts have indicated may not be sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)); a commonplace business method being applied on a general purpose computer, gathering and analyzing information using conventional techniques and displaying the result, and selecting a particular generic function for computer hardware to perform from within a range of fundamental or commonplace functions performed by the hardware, which courts have indicated may not be sufficient to show an improvement to technology (see MPEP 2106.05(a)(II)); a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, which do not qualify as a particular machine or use thereof (see MPEP 2106.05(b)(I)); a machine that is merely an object on which the method operates, which does not integrate the exception into a practical application (see MPEP 2106.05(b)(II)); use of a machine that contributes only nominally or insignificantly to the execution of the claimed method, which does not integrate a judicial exception (see MPEP 2106.05(b)(III)); transformation of an intangible concept such as a contractual obligation or mental judgment, which is not likely to provide significantly more (see MPEP 2106.05(c)); remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, which courts have found to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome (see MPEP 2106.05(f)); use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea, a commonplace business method or mathematical algorithm being applied on a general purpose computer, and requiring the use of software to tailor information and provide it to the user on a generic computer, which courts have found to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process (see MPEP 2106.05(f)); mere data gathering in the form of obtaining information about transactions using the Internet to verify transactions and consulting and updating an activity log, and selecting a particular data source or type of data to be manipulated in the form of selecting information, based on types of information and availability of information in an environment, for collection, analysis, and display, which courts have found to be insignificant extra-solution activity (see MPEP 2106.05(g)); and specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, which courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). For at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong Two of the eligibility analysis.
The next step of the eligibility analysis, Step 2B, asks whether a claim recites additional elements that amount to significantly more than the judicial exception. (See MPEP 2106.05(II).) The step involves identifying whether there are any additional elements in the claim beyond the judicial exceptions, and evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept. (See id.) The ineligibility rationales applied at Step 2A, Prong Two, also apply to Step 2B. (See id.) For all of the reasons covered in the analysis performed at Step 2A, Prong Two, claim 1 fails to meet the criteria of Step 2B. Further, claim 1 also fails to meet the criteria of Step 2B because at least some of the additional elements are analogous to: receiving or transmitting data over a network, and storing and retrieving information in memory, which courts have recognized as well-understood, routine, conventional activity, and as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). As a result, claim 1 is rejected under 35 USC 101 as ineligible for patenting.
Regarding claims 2-10, the claims depend from claim 1, and expand upon limitations introduced by claim 1. The dependent claims are rejected at least for the same reasons as claim 1. For example, the dependent claims recite abstract idea elements similar to the abstract idea elements of claim 1, that fall under the same abstract idea groupings as the abstract idea elements of claim 1, and/or that fall under the mathematical concepts grouping (e.g., the “wherein receiving the first transaction data comprises: retrieving the first transaction data ... comprises aggregated historical transaction datasets from multiple sources from multiple users” of claim 2, the “wherein: determining the first micro-intents associated with the first transaction data comprises: using a transpose of a matrix comprising the first transaction data and a transpose of a respective mean vector for each item of the first items; and creating a respective correlation matrix from a respective diagonal matrix for each item of the first items and a respective covariance matrix for each item of the first items” of claim 3, the “wherein: determining the first micro-intents associated with the first transaction data of the first transactions further comprises: using eigenvectors corresponding to a respective eigenvalue decomposition of the respective correlation matrix, wherein the respective eigenvalue decomposition of the respective correlation matrix comprises percentages of transformed vectors” of claim 4, the “wherein the operations further comprise: creating, using the first transaction data, a first transaction data matrix, wherein rows of the first transaction data matrix correspond to transactions of the first transactions and columns of the first transaction data matrix correspond to items of the first items; creating a respective mean vector for each item of the first items using the first transaction data matrix; creating a respective covariance matrix using the first transaction data matrix and the respective mean vector for each item of the first items; creating a respective diagonal matrix of respective diagonal matrixes for the respective covariance matrix for each item of the first items, wherein diagonals of the respective diagonal matrix for each item of the first items are equal to that of the respective covariance matrix for each item of the first items; creating respective eigenvalue decompositions of each of respective correlation matrixes, wherein columns of the respective eigenvalue decompositions of the respective correlation matrixes are respective eigenvectors of the respective correlation matrixes, and the respective eigenvectors of the respective correlation matrixes represent the first micro-intents; and grouping the first micro-intents into clusters use one or more of: the first transaction data matrix; the respective mean vectors; the respective diagonal matrixes; or the respective eigenvectors” of claim 5, the “wherein the operations further comprise: localizing and scaling each transaction of the first transactions using a first transaction data matrix, a respective mean vector for each item of the first items, and a respective diagonal matrix for each item of the first items” of claim 6, the “wherein the operations further comprise: determining, using the first micro-intents for the first transactions, a label pattern for the user, wherein the label pattern comprises at least one of: a new interest; or an evolving preference” of claim 7, the “wherein the operations further comprise: applying missed replenishment cycle methods to determine a periodicity of the current micro-intent” of claim 8, the “wherein the operations further comprise: determining a product associated with at least one label not in current transaction data, wherein the ... element is a product promotion for the product” of claim 9, and the “wherein: the current transaction data comprises datasets of a number of items currently added ...; and the first micro-intents and the second micro-intents in the current transaction data are ordered by a hazard rate” of claim 10). The dependent claims recite further additional elements that are similar to the additional elements of claim 1, that fail to warrant eligibility for the same reasons as the additional elements of claim 1 (e.g., the “system” of claims 2-10, the “transaction database” of claim 2, and the “to an electronic shopping cart of the user during the current browsing session of the website” of claim 10). Accordingly, claims 2-10 also are rejected as ineligible under 35 USC 101.
Regarding claims 11-20, while the claims are of different scope relative to claims 1-10, the claims recite limitations similar to the limitations of claims 1-10. As such, the rejection rationales applied to reject claims 1-10 also apply for purposes of rejecting claims 11-20. Claims 11-20 are, therefore, also rejected as ineligible under 35 USC 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1, 2, 7-9, 11, 12, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. App. Pub. No. 2020/0034858 A1 to Chandra Sekar Rao et al. (hereinafter referred to as “Chandra Sekar Rao”), in view of U.S. Pat. No. 10,643,246 B1 to Suprasadachandran Pillai (hereinafter referred to as “Suprasadachandran Pillai”).
Regarding claim 1, Chandra Sekar Rao discloses the following limitations:
“A system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising: ...” - The “processing platform” (para. [0075]), “processor” (para. [0078]), and “memory” and “executable program code” (para. [0079]), in Chandra Sekar Rao, read on the recited limitation.
“... receiving, using a distributed network comprising a distributed memory architecture to reduce congestion while allowing data access, first transaction data of first transactions for first items; ...” - Chandra Sekar Rao discloses, “The database 106 in the present embodiment is implemented using one or more storage systems associated with the dynamic customer intent and recommendation determination system 105. Such storage systems can comprise any of a variety of different types of storage including network-attached storage (NAS), storage area networks (SANs), direct-attached storage (DAS) and distributed DAS, as well as combinations of these and other storage types, including software-defined storage” (para. [0021]), “At least one embodiment of the invention includes leveraging such information to predict whether a particular user will order and/or purchase one or more particular products/services related to an ongoing web browsing session of that user. Additionally, if it is predicted that the user is not expected to order and/or purchase the one or more offerings in question, then one or more embodiments of the invention can include utilizing current and historical data (pertaining to both this particular user and one or more other users) to classify and/or profile the user as one or more user types or personas. Such a classification of the user can then be leveraged to dynamically recommend one or more other products/services to the user (in the web browsing session) that the user may be likely to order/purchase” (para. [0037]), “The underlying physical machines may comprise one or more distributed processing platforms that include one or more storage systems” (para. [0072]), and “For example, particular types of storage products that can be used in implementing a given storage system of a distributed processing system in an illustrative embodiment include VNX® and Symmetrix MAX® storage arrays, software-defined storage products such as ScaleIO™ and ViPR®, all-flash and hybrid flash storage arrays such as Unity™, cloud storage products such as Elastic Cloud Storage (ECS), object-based storage products such as Atmos®, scale-out all-flash storage arrays such as XtremIO™, and scale-out NAS clusters comprising Isilon® platform nodes and associated accelerators, all from Dell EMC. Combinations of multiple ones of these and other storage products can also be used in implementing a given storage system in an illustrative embodiment” (para. [0089]). The distributed storage providing data for the distributed processing, wherein the data includes historical data used to classify users as personas, in Chandra Sekar Rao, reads on the recited limitation.
The combination of Chandra Sekar Rao and Suprasadachandran Pillai (hereinafter referred to as “Chandra Sekar Rao/Suprasadachandran Pillai”) teaches limitations below of claim 1 that do not appear to be disclosed in their entirety by Chandra Sekar Rao:
“... determining, based on the first transaction data, first micro-intents associated with the first transactions; ...” - See the aspects of Chandra Sekar Rao that have been cited above. Chandra Sekar Rao also discloses, “A model such as model 132 can be trained (based, for example, on the list of pages a user/visitor visits and actions performed during a conversion process) to understand the persona of a visitor browsing a given website. Examples of such personas can include the following: Persona1: Easily frustrated, and opinionated; Persona2: Responds to offers and deals; Persona3: Researches many aspects of a product; Persona4: Returns items at a much higher level than most users; Persona5: A high-value lapsed purchaser; Persona6: Shopped previously but will not and/or has not returned; and Persona7: Views emails, clicks-through to website and looks into suggested products” (para. [0042]). Suprasadachandran Pillai discloses, “In one example, a new mother may pass through a series of different stages in quick succession of short spans (e.g., expecting mom, feeding mom, toddler's mom and/or the like). In some examples, the profile 206 may be a personalized customer persona for the mother that may be associated with being an expecting mom. This association may be performed by the customized user profile management system 110, and may be based on historical data associated with the personalized customer persona as well as historical data associated with the browse and purchase activity of other users having similar interest. In one example, the mother may select a pre-generated user profile persona from a plurality of available pre-generated user profile personas. In one example, the pre-generated user profile personas may be determined based on trends among a plurality of customers and/or among all customers. Generally, trends associated with customers may be clustered based on vector similarity. Pre-generated customized user profile personas may then be created based on data from the cluster having a similarity measure above a threshold. Naturally, profiles clustered in the same cluster are likely to have interest in similar products. For example, expecting mothers are likely to be interested in pregnancy products, while toddler mothers are likely to be interested in toddler toys and/or the like” (col. 8, ll. 14-37), and “The process 500 may continue with providing to the second device, data associated with the first user account, the data associated with the first user account comprising first user profile data for a first user profile associated with a first user, the first user profile being associated with the first account, wherein the first user profile data comprises historical data associated with the first user, one or more recommended products for the first user, and one or more user preferences associated with the first user profile, wherein the first user persona is a pre-defined persona that is determined based at least in part on clustered historical data associated with a plurality of user profiles (506)” (col. 18, ll. 52 to 63). The use of historical data to classify users into personas, wherein the personas are indicative of potential actions that users may take, in Chandra Sekar Rao, and the use of historical data of users to generate personas, in Suprasadachandran Pillai, reads on the recited limitation.
“... determining one or more second micro-intents associated with second transactions; ...” - See the aspects of Chandra Sekar Rao and Suprasadachandran Pillai that have been cited above. The use of any additional historical data to generate personas, as in Suprasadachandran Pillai, and to classify users into personas, as in Chandra Sekar Rao, reads on the recited limitation.
“... receiving current transaction data of a user transaction from a user interface of an electronic device of a user during a current browsing session of a website; ...” - See the aspects of Chandra Sekar Rao that have been cited above. The receiving, by the network infrastructure, of “a sequence of browsing session events” (para. [0041]), from “displays” and “user interfaces” (para. [0022]), “during a user browsing session of one or more web pages” (para. [0003]), in Chandra Sekar Rao, reads on the recited limitation.
“... determining, based on the current transaction data, one or more third micro-intents associated with the user transaction; ...” - See the aspects of Chandra Sekar Rao that have been cited above. Chandra Sekar Rao also discloses, “MAPPING THE USER BROWSING SESSION TO ONE OR MORE PRE-ESTABLISHED PROCUREMENT USER TYPES” (FIG. 7). Performing the mapping, based on the current browsing session events of the user, in Chandra Sekar Rao, reads on the recited limitation.
“... determining that the user is expressing a current micro-intent based on the first micro-intents, the one or more second micro-intents, and the one or more third micro-intents; and ...” - See the aspects of Chandra Sekar Rao and Suprasadachandran Pillai that have been cited above. Chandra Sekar Rao also discloses, “Such a classification of the user can then be leveraged to dynamically recommend one or more other products/services to the user (in the web browsing session) that the user may be likely to order/purchase” (para. [0037]), and “DETERMINING A RECOMMENDATION OF ONE OR MORE OFFERINGS DISTINCT FROM THE PARTICULAR OFFERING, BASED AT LEAST IN PART ON THE GENERATED PREDICTION AND THE MAPPING OF THE USER BROWSING SESSION TO ONE OR MORE PROCUREMENT USER TYPES” (FIG. 7). Determining that the user may order/purchase the product/service based on the generated prediction, the historical data-based mapping, and the current data-based mapping, in Chandra Sekar Rao (and Suprasadachandran Pillai for additional historical data-based mapping), reads on the recited limitation.
“... transmitting an instruction to display a user interface element on the user interface of the electronic device, wherein the user interface element is correlated with at least one of the first micro-intents, the one or more second micro-intents, or the one or more third micro-intents.” - See the aspects of Chandra Sekar Rao and Suprasadachandran Pillai that have been cited above. Chandra Sekar Rao also discloses, the “outputting, within the user browsing session, the recommendation” (para. [0058]) of “offerings” (para. [0056]) to the display and the user interface, wherein the displayed data relates to “pre-established procurement user types mapped to the user browsing session” (para. [0056]), in Chandra Sekar Rao, wherein the procurement user types are generated and used based on historical user data, per Chandra Sekar Rao and Suprasadachandran Pillai),reads on the recited limitation.
Suprasadachandran Pillai discloses “to better target advertisement, products and product recommendations to customers” (col. 5, ll. 29-31), similar to the claimed invention and to Chandra Sekar Rao. it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the historical data-based persona aspects, of Chandra Sekar Rao, to include the historical data-based personal aspects, of Suprasadachandran, for better targeting of customers, per Suprasadachandran Pillai (col. 5, ll. 29-31).
Regarding claim 2, Chandra Sekar Rao/Suprasadachandran Pillai teaches the following limitations:
“The system of claim 1, wherein receiving the first transaction data comprises: retrieving the first transaction data from a transaction database, wherein the transaction database comprises aggregated historical transaction datasets from multiple sources from multiple users.” - See the aspects of Chandra Sekar Rao that have been cited above. Additionally, the directional arrow from “BROWSING SESSION DATA” (FIG. 2) of “DATABASE” (FIG. 1), the browsing session data describing conversion transactions for products of users, wherein the database includes “current and historical data (pertaining to both this particular user and one or more other users” (para. [0037]), in Chandra Sekar Rao, reads on the recited limitation.
Regarding claim 7, Chandra Sekar Rao/Suprasadachandran Pillai teaches the following limitations:
“The system of claim 1, wherein the operations further comprise: determining, using the first micro-intents for the first transactions, a label pattern for the user, wherein the label pattern comprises at least one of: a new interest; or an evolving preference.” - See the aspects of Chandra Sekar Rao that have been cited above. The determining, using the cloud infrastructure and using the customer intents for conversion transactions, “patterns of actions performed by users/visitors” (para. [0042]), wherein the patterns of actions relate to “the persona of a visitor” (para. [0042]), in Chandra Sekar Rao, reads on the recited limitation.
Regarding claim 8, Chandra Sekar Rao/Suprasadachandran Pillai teaches the following limitations:
“The system of claim 1, wherein the operations further comprise: applying missed replenishment cycle methods to determine a periodicity of the current micro-intent.” - See the aspects of Chandra Sekar Rao that have been cited above. Applying methodology involving “Persona5: A high-value lapsed purchaser; Persona6: Shopped previously but will not and/or has not returned” (para. [0042]), and “whether or not the user returns to one or more relevant web pages after some period of time has elapsed” (para. [0039]), in Chandra Sekar Rao, reads on the recited limitation.
Regarding claim 9, Chandra Sekar Rao/Suprasadachandran Pillai teaches the following limitations:
“The system of claim 1, wherein the operations further comprise: determining a product associated with at least one label not in current transaction data, wherein the user interface element is a product promotion for the product.” - See the aspects of Chandra Sekar Rao that have been cited above. Additionally, the determining, using the cloud infrastructure, “other products can include distinct and/or supplementary products to the offerings analyzed as part of the intention prediction, rather than simply generic or alternative versions of the offering in question” (para. [0037]), wherein data displayed on the user interface is a recommendation for the offering, in Chandra Sekar Rao, reads on the recited limitation.
Regarding claims 11, 12, and 17-19, while the claims are of different scope relative to claims 1, 2, and 7-9, the claims recite limitations similar to those recited by claims 1, 2, and 7-9. As such, the rationales applied to reject claims 1, 2, and 7-9 also apply for purposes of rejecting claims 11, 12, and 17-19. Claims 11, 12, and 17-19 are, therefore, also rejected under 35 USC 103 as obvious in view Chandra Sekar Rao/Suprasadachandran/Pillai.
Claims 3, 6, 13, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chandra Sekar Rao, in view of Suprasadachandran Pillai, and further in view of U.S. Pat. No. 8,595,089 B1 to Roberts (hereinafter referred to as “Roberts”).
Regarding claim 3, the combination of Chandra Sekar Rao, Suprasadachandran Pillai, and Roberts (hereinafter referred to as “Chandra Sekar Rao/Suprasadachandran/Roberts”) teaches limitations below that do not appear to be taught in their entirety by Chandra Sekar Rao/Suprasadachandran:
“The system of claim 1, wherein: determining the first micro-intents associated with the first transaction data comprises: using a transpose of a matrix comprising the first transaction data and a transpose of a respective mean vector for each item of the first items; and ...” - See the aspects of Chandra Sekar Rao that have been cited above. Additionally, the use of the “matrix transpose” (col. 4, ll. 35-37), including data related to “the mean vector” (col. 4, l. 27) of data, in Roberts, in the context of the conversion transaction data for products, in Chandra Sekar Rao, reads on the recited limitation.
“... creating a respective correlation matrix from a respective diagonal matrix for each item of the first items and a respective covariance matrix for each item of the first items.” - See the aspects of Chandra Sekar Rao and Roberts that have been cited above. Additionally, the creating of the “kt x k matrix given by I with rows corresponding to indices” (col. 4, ll. 33-35), involving the “k x k diagonal matrix” (col. 4, ll. 35-37) and the “covariance matrix” (col. 4, l. 28), in Roberts, in the context of the products, in Chandra Sekar Rao, reads on the recited limitation.
Roberts discloses a “product recommender system” (Abstract), similar to the claimed invention and to Chandra Sekar Rao/Suprasadachandran Pillai. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the determining, performed by Chandra Sekar Rao/Suprasadachandran Pillai, to include use of the matrix-related aspects, of Roberts, for its “high performance without the disadvantages,” per Chandra Sekar Rao (col. 2, ll. 39 and 40).
Regarding claim 6, Chandra Sekar Rao/Suprasadachandran Pillai/Roberts teaches limitations below that do not appear to be taught in their entirety by Chandra Sekar Rao/Suprasadachandran Pillai:
“The system of claim 5, wherein the operations further comprise: localizing and scaling each transaction of the first transactions using the first transaction data matrix, the respective mean vector for each item of the first items, and the respective diagonal matrix for each item of the first items.” - See the aspects of Chandra Sekar Rao and Roberts that have been cited above. The processing of the historical user data related to user browsing sessions and conversion activities, of Chandra Sekar Rao, when modified to include use of the “data-matrix” (col. 1, ll. 38-48), “mean vector” (col. 4, ll. 19-26), and “diagonal matrix” (col. 4, ll. 27-43), of Roberts, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 3, also apply to this rejection of claim 6.
Regarding claims 13 and 16, while the claims are of different scope relative to claims 3 and 6, the claims recite limitations similar to those recited by claims 3 and 6. As such, the rationales applied to reject claims 3 and 6 also apply for purposes of rejecting claims 13 and 16. Claims 13 and 16 are, therefore, also rejected under 35 USC 103 as obvious in view of Chandra Sekar Rao/Suprasadachandran Pillai/Roberts.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Chandra Sekar Rao, in view of Suprasadachandran Pillai, further in view of Roberts, and further in view of U.S. Pat. App. Pub. No. 2011/0040601 A1 to Bai et al. (hereinafter referred to as “Bai”).
Regarding claim 4, the combination of Chandra Sekar Rao, Suprasadachandran Pillai, Roberts, and Bai (hereinafter referred to as “Chandra Sekar Rao/Suprasadachandran Pillai/Roberts/Bai”) teaches limitations below that do not appear to be taught in their entirety by Chandra Sekar Rao/Suprasadachandran Pillai/Roberts:
“The system of claim 3, wherein: determining the first micro-intents associated with the first transaction data of the first transactions further comprises: using eigenvectors corresponding to a respective eigenvalue decomposition of the respective correlation matrix, wherein the respective eigenvalue decomposition of the respective correlation matrix comprises percentages of transformed vectors.” - See the aspects of Chandra Sekar Rao and Roberts that have been cited above. Additionally, the use of the “eigen-vector” (para. [0026]) corresponding to “Eigen-value Decomposition” (para. [0026]) and “matrix C” (para. [0025]), wherein such element result in values of any kind, in Bai, when applied in the context of the determining and matrix-related aspects of Chandra Sekar Rao/Roberts, reads on the recited limitation.
Bai discloses “customer segmentation” (Abstract), similar to the claimed invention and Chandra Sekar Rao/Suprasadachandran Pillai/Roberts. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the determining and matrix-related aspects of Chandra Sekar Rao/Suprasadachandran Pillai/Roberts, to include the eigen-vector, eigen-value decomposition, and matrix aspects of Bai, because “The problem can be solved by many algorithms, but one of the most efficient ways is generalized eigen-value decomposition approach,” per Bai (para. [0026]).
Regarding claim 14, while the claim is of different scope relative to claim 4, the claim recites limitations similar to those recited by claim 4. As such, the rationales applied to reject claim 4 also apply for purposes of rejecting claim 14. Claim 14 is, therefore, also rejected under 35 USC 103 as obvious in view of Chandra Sekar Rao/Suprasadachandran Pillai/Roberts/Bai.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chandra Sekar Rao, in view of Suprasadachandran, and further in view of U.S. Pat. App. Pub. No. 2017/0061484 A1 to Jen et al. (hereinafter referred to as “Jen”).
Regarding claim 10, the combination of Chandra Sekar Rao, Suprasadachandran Pillai, and Jen (hereinafter referred to as “Chandra Sekar Rao/Suprasadachandran/Jen”) teaches limitations below that do not appear to be taught in their entirety by Chandra Sekar Rao/Suprasadachandran Pillai:
“The system of claim 9, wherein: the current transaction data comprises datasets of a number of items currently added to an electronic shopping cart of the user during the current browsing session of the website; and the first micro-intents and the second micro-intents in the current transaction data are ordered by a hazard rate.” - See the aspects of Chandra Sekar Rao that have been cited above. Additionally, the browsing pattern data about conversion transactions including data about “whether a product/service has been added to a shopping cart within the browsing session” (para. [0039]), and the customer intents expressed via the browsing pattern data in Chandra Sekar Rao being tied to “Next Purchase Hazard Rate” (para. [0092]) and “rank of hazard rate” (para. [0099]), in Jen, reads on the recited limitation.
Jen discloses “determining a next purchase interval of a customer” (Abstract), similar to the claimed invention and to Chandra Sekar Rao/Suprasadachandran Pillai. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the analysis of user data, in Chandra Sekar Rao/Suprasadachandran Pillai, to include consideration of the hazard rate aspects, of Jen, to “determine a proper time to effectively engage customers,” as taught by Jen (para. [0020]).
Regarding claim 20, while the claim is of different scope relative to claim 10, the claim recites limitations similar to those recited by claim 10. As such, the rationales applied to reject claim 10 also apply for purposes of rejecting claim 20. Claim 20 is, therefore, also rejected under 35 USC 103 as obvious in view of Chandra Sekar Rao/Suprasadachandran Pillai/Jen.
Examiner’s Remarks
This Office Action does not include any prior art rejections of claims 5 and 15. With respect to these claims, the closest prior art of record is represented by the various combinations of Chandra Sekar Rao, Roberts, Bai, and Jen that have been outlined in the 35 USC 102 and 35 USC 103 sections above. Using claim 5 as an example, while the combinations of the cited references teach elements recited by claim 5, including the transaction data, matrix, mean vector, covariance matrix, diagonal matrix, eigenvalue decomposition, eigenvector, and cluster aspects of the claim, the cited references do not teach the recited relationships between the aspects, wherein the recited relationships are established by presentation of the term “using” and other terminology relating the aspects, of the claim. As such, claim 5 appears to distinguish over the closest prior art of record. Claim 15, while of different scope, recite limitations similar to those of claim 5, and are viewed as distinguishing over the closest prior art of record for similar reasons.
Response to Arguments
On pp. 12 and 13 of the Response, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 101. The applicant contends that the claims of the present application are eligible for the same reasons the claims of the parent application are eligible. The examiner disagrees. While the claims of the present application are similar to the claims of the parent application, there are significant and substantive differences. The differences, when viewing each of the claims as a whole, warrants rejecting the claims of the present application. The rejection is maintained for the reasons specified in the 35 USC 101 section above.
On pp. 13 and 14 of the Response, the applicant requests reconsideration and withdrawal of the claim rejections under 35 USC 102 and 103. The applicant’s contentions regarding the rejections are moot in view of Suprasadachandran Pillai (see the 35 USC 103 section above), as the claimed multiple micro-intents are disclosed, taught, or suggested, at least in part, by the newly-cited reference.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/THOMAS YIH HO/Primary Examiner, Art Unit 3624
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