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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. The applicant's submission, the Response filed on 25 June 2026 (hereinafter referred to as the "Response"), has been entered.
Status of the Claims
The pending claims in the present application are claims 1-20, as presented in the Response.
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 2106.1
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 independent claim 1 as an example, the claim recites the following abstract idea limitations:
“... to perform operations comprising: receiving ... first transaction data of first transactions for first items; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... determining, based on the first transaction data, first micro-intents that are present in first actions performed via the internet and indicated by the first transactions without knowledge of users expressing the micro-intents; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... determining one or more second micro-intents that are present in second actions and indicated by second transactions; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... receiving current transaction data of a user transaction from ... a user during a current browsing session of a website; ...” - See below regarding MPEP 2106.04(a), 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), 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), certain methods of organizing human activity, and mental processes
“... transmitting an instruction to display a ... element ..., wherein the ... 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 below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
The above-listed limitations of independent 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 “transmitting” 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 independent claim 1 as an example, the claim recites the following additional element limitations:
The claimed “to 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” - 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” - 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 claimed “element” is a “user interface element" - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The above-listed additional element limitations of independent 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, independent 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 independent 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 (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 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 also recite other abstract idea elements that fall under the mathematical concepts grouping (e.g., 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, respective diagonal matrixes for the first items, wherein entries of a respective diagonal matrix, of the respective diagonal matrixes, for an item, of the first items, is equal to a respective covariance matrix for the item; creating respective eigenvalue decompositions for the first items; and grouping the first micro-intents into clusters use one or more of: the respective diagonal matrixes; or the respective eigenvectors” of claim 5, and 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 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, and the “transaction database” of claim 2). 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. App. Pub. No. 2014/0278974 A1 to Standish (hereinafter referred to as "Standish").
Regarding independent claim 1, Chandra Sekar Rao discloses elements reading on 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]), "One or more embodiments of the invention include monitoring and tracking user search/browsing patterns such as, for example, time spent per product, time spent per page, search frequency, number of return visits, etc. Additionally, such an embodiment can also include learning the pages visited by converting users (that is, users who ultimately converted browsing activity into one or more orders and/or purchases via a related website) prior to such conversion-related transactions, and indexing the learned pages and one or more pages related thereto" (para. [0036]), "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]), "As web browsing can be inherently sequential in nature, at least one embodiment of the invention include utilizing RNNs in analyzing web browsing sequences. By way merely of illustration, such sequences can include actions such as the means by which a user accessed/entered a website (direct load, email-click, advertisement-click, etc.), which product/service pages are visited by the user, whether or not the user accessed any product/service reviews, the percentage of a web page read and/or viewed by the user, the amount of time spent on a given web page, whether a product/service has been marked for order/purchase (for example, whether a product/service has been added to a shopping cart within the browsing session), etc. Additionally, such an embodiment additionally monitors and/or tracks whether or not a user ultimately converts his or her web browsing activities into an order/purchase. If a user does not ultimately make the conversion, one or more embodiments of the invention can monitor and/or track whether or not the user returns to one or more relevant web pages after some period of time has elapsed" (para. [0039]), “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 of users, including their browsing actions, 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.
The combination of Chandra Sekar Rao and Standish (hereinafter referred to as “Chandra Sekar Rao/Standish”) teaches aspects reading on the following limitations of independent claim 1:
“... determining, based on the first transaction data, first micro-intents that are present in first actions performed via the internet and indicated by the first transactions without knowledge of users expressing the micro-intents; ...” - See the aspects of Chandra Sekar Rao that have been cited above. Chandra Sekar Rao also discloses, "As detailed herein, one or more embodiments of the invention include predicting the intention of a user during a web browsing session as to whether the user's browsing activities will be converted into an order of one or more products/services or not" (para. [0035]), “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]), "Accordingly, FIG. 4 depicts known user browsing patterns by persona 402, which can be used to match browsing patterns to personas for unknown user browsing patterns by user/visitor identifier (ID) 404. Determining such browsing patterns, as illustrated in FIG. 4, can include searching key words, determining an amount of time spent on product description web pages, identifying searched-for product categories and related solutions/services, monitoring actions on different web pages, and access/entrance mechanisms (campaigns, form-filling, ad-clicks, etc.). Such determinations can be based at least in part on captured data such as identity of pages visited 406 by the user(s) and one or more attributes of and/or actions taken during one or more browsing sessions 408" (para. [0043]). The examiner views determining, based on actions taken by users during browsing sessions, including browsing activity related to conversions, personas for browsing activity on websites, wherein the personas are based on the actions taken by users during browsing sessions, and wherein the personas are not known to the users, in Chandra Sekar Rao, as reading on the recited limitation. For example, the "Persona4: Returns items at a much higher level than most users" of Chandra Sekar Rao equates to intent to return items, which is described in the context of determining user intents by Chandra Sekar Rao. The examiner acknowledges, however, that the personas are not explicitly associated with intents of the users in Chandra Sekar Rao. Standish discloses "Personas" and "real-time intent" (Abstract), similar to the claimed invention and to Chandra Sekar Rao. Standish also discloses, more specifically, "This application discloses, among other things, techniques for assessing Digital Body Language, which may aggregate structured and unstructured data from multiple sources, automatically abstracting data into easy-to-use 360-degree graphic representations of individuals, groups, events and communities called "Personas." Personas may be analyzed to identify "demand signals," which may indicate real-time intent and interest for specific brands, products and services" (Abstract). Relating users with personas based on their actions during browsing, as in Chandra Sekar Rao, and analyzing the personas to identify demand signals indicating real-time intent of consumers, as in Standish, establishes the link between personas and intent. 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 relating of personas and users, of Chandra Sekar Rao, to include the analyzing of personas to identify demand signals indicating real-time intent of consumers, of Standish, so that " marketers may be empowered to authentically engage with these individuals and prospect clusters by delivering personalized offers via their preferred digital channels. Through a better understanding of consumer purchase intent, marketers may be enabled to initiate more timely and relevant interactions, which may result in improved revenue conversion and return-on-investment (ROI)," per Standish (Abstract). Additionally or alternatively, the modification constitutes: combining prior art elements according to known methods to yield predictable results; use of known technique to improve similar devices (methods, or products) in the same way; and/or applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (see MPEP 2143(I)).
“... determining one or more second micro-intents that are present in second actions and indicated by second transactions; ...” - See the aspects of Chandra Sekar Rao and Standish that have been cited above. Relating users with other personas based on other actions taken by users during browsing, including one or more of multiple types of browsing actions, as in Chandra Sekar Rao, and analyzing the other personas to identify demand signals indicating real-time intent of consumers, per Standish, reads on the recited limitation. The rationales for combining the cited references, as set forth above, also apply to this 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 and Standish 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 of users to personas, based on the current browsing session events and actions of the users, in Chandra Sekar Rao, and analyzing the personas to identify demand signals indicating real-time intent of consumers, per Standish, reads on the recited limitation. The rationales for combining the cited references, as set forth above, also apply to this 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 Standish 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 persona mapping, in Chandra Sekar Rao, and analyzing the personas to identify demand signals indicating real-time intent of consumers, per Standish, reads on the recited limitation. The rationales for combining the cited references, as set forth above, also apply to this 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 Standish 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 (personas) are generated and used based on historical and current user data, per Chandra Sekar Rao, and analyzing the personas to identify demand signals indicating real-time intent of consumers, per Standish, reads on the recited limitation. The rationales for combining the cited references, as set forth above, also apply to this limitation.
Regarding claim 2, Chandra Sekar Rao/Standish teaches aspects reading on 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/Standish teaches aspects reading on 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/Standish teaches aspects reading on 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/Standish teaches aspects reading on 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/Standish.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chandra Sekar Rao, in view of Standish, 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, Standish, and Jen (hereinafter referred to as “Chandra Sekar Rao/Standish/Jen”) teaches aspects reading on the following limitations:
“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. Chandra Sekar Rao also discloses 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]). That, and the customer intents expressed via the browsing pattern data and personas, in Chandra Sekar Rao, reads on the recited "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" limitation. Chandra Sekar Rao does not appear to explicitly disclose any relationship to hazard rates. Jen discloses "a method of digital marketing" (para. [0002]), similar to the claimed invention and to Chandra Sekar Rao. Jen also discloses, “Next Purchase Hazard Rate” (para. [0092]), "Next purchase hazard rate indicates the probability that the i-th customer purchases again in X-day after his/her last purchase date in the channel. X is a positive integer. In one embodiment, X may be 7 or 30, so that the system 100 may determine the hazard rate of the i-th customer weekly or monthly. Calculation of the next purchase hazard rate is based on a hazard rate function and the mean purchase interval E(t.sub.i) is used as an input to the hazard rate function. By calculating the next purchase hazard rate, the system 100 may more effectively engage the i-th customer. Moreover, every time when the i-th customer makes purchases in the channel, the system 100 updates the corresponding mean purchase interval E(t.sub.i) and next purchase hazard rate" (para. [0093]), "FIG. 9 shows exemplary hazard rate analysis according to one embodiment of the present disclosure. FIG. 9 illustrates the system 100 using the hazard rate function h(t.sub.i) to evaluate the time to engage the i-th customer" (para. [0097]), and “Accordingly, the marketing system may only engage with customers having a higher hazard rate than the minimal hazard rate on the analyze date. The minimal hazard rate may be a minimal reasonable value that the system 100 determined according to the hazard rate statistics of all customers in the channel. For example, if the system 100 sets the minimal hazard rate to be 0.5, then the marketing system chooses customers who have higher rates than 0.5 to engage on the specific date. In other embodiment, the system 100 may also determine the minimal hazard rate based on the customers having top hazard rates. For example, if the system 100 only chooses customers with top one-thousand hazard rates to engage, the hazard rate of the customer who has the 1000.sup.th or 1001.sup.th rank of hazard rate is the minimal hazard rate for such specific date” (para. [0099]). The ranking of hazard rates by differentiating those above thresholds with those below them, for making purchase predictions, in Jen, when applied to making similar predictions, in Chandra Sekar Rao, reads on the recited limitation. 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 predicting of whether users will order offerings during user browsing sessions, of Chandra Sekar Rao, to include the hazard rate considerations and ranking, of Jen, as doing so provides for more effective engagement of customers, per Jen (see para. [0093]). Additionally or alternatively, the modification constitutes: combining prior art elements according to known methods to yield predictable results; use of known technique to improve similar devices (methods, or products) in the same way; and/or applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (see MPEP 2143(I)).
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/Standish/Jen.
Examiner’s Remarks
This Office Action does not include any prior art rejections of claims 3-6 and 13-16.
With respect to claims 3, 4, 6, 13, 14, and 16, the closest prior art of record is represented by Chandra Sekar Rao and U.S. Pat. No. 8,595,089 B1 of Roberts (hereinafter referred to as "Roberts"). Using claim 3 as an example, while the combination of Chandra Sekar Rao and Roberts teaches elements recited by claim 3, including the matrix transpose, mean vector, and various matrices, Roberts discloses their use in "A product recommender system where product ratings from a plurality of users are represented as plurality of vectors is disclosed. The ratings vectors are represented by a mean vector and a covariance matrix. The mean vector and the covariance matrix are estimated from a data-set of known product ratings. Product ratings are predicted using the mean vector and the covariance matrix" (Abstract). It would not have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to apply those aspects of Roberts to determine user intents to purchase products, based on their browsing actions, of Chandra Sekar Rao, which is unrelated to predicting product ratings. Claim 3, therefore, distinguishes over the closets prior art of record. Claim 4 depends from claim 3, and also distinguishes over the closest prior art of record at least do to its dependency. Claim 6, while of different scope, recites limitations similar to those of claim 3, and distinguishes over the closest prior art of record for that reason. The same holds true for claims 13, 14, and 16, which are similar to claims 3, 4, and 6.
With respect to claims 5 and 15, the closest prior art of record is represented by Chandra Sekar Rao and U.S. Pat. App. Pub. No. 2011/0040601 A1 to Bai et al. (hereinafter referred to as "Bai"). Using claim 5 as an example, while the combination of Chandra Sekar Rao and Bai teaches elements recited by claim 5, including the transaction data, diagonal matrix, covariance matrix, eigenvalue decomposition, eigenvector, and cluster aspects of the claim, the cited references do not disclose, teach, or suggest the recited relationships between the aspects, as claimed. 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. 10-13 of the Response, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 101, and the claim rejections under 35 USC 103, in view of what was discussed during the most recent interview, and in view of various arguments.
With respect to the claim rejection under 35 USC 101, the applicant emphasizes the fact that the examiner agreed to withdraw the rejection, based on the eligibility rationale from Amdocs. The examiner initially agreed due to similarities between limitations of the claimed invention (e.g., the recited "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" limitation of independent claim 1), and this example of an eligible improvement in computer-functionality from MPEP 2106.05(a)(I): "A distributed network architecture operating in an unconventional fashion to reduce network congestion while generating networking accounting data records, Amdocs (Israel), Ltd. v. Openet Telecom, Inc., 841 F.3d 1288, 1300-01, 120 USPQ2d 1527, 1536-37 (Fed. Cir. 2016)." Upon closer inspection and consideration of the Amdocs decision, the examiner no longer agrees that the eligibility rationale from Amdocs is applicable to the applicant's claimed invention. The examiner now views the claim language quoted above as being only superficially related to the eligibility rationale from Amdocs. The relationship is superficial because while the language of the claim and the language of the eligibility rationale are similar, consideration of the context for the language in Amdocs shows that eligibility therein should not be applied to the applicant's claimed invention. With respect to the patents at issue in Amdocs, there is an eligible improvement to computer functionality based on there being a technological solution to a technological problem. In the Amdocs patents, the problem is clearly stated: "Importantly, the distributed data gathering, filtering and enhancements performed in the system 100 enables load distribution. Granular data can reside in the peripheries of the system 100, close to the information sources. This helps avoids reduce congestion in network bottlenecks but still allows the data to be accessible from a central location. In previous systems, all the network information flows to one location, making it very difficult to keep up with the massive record flows from the network devices and requiring huge databases" (see, e.g., col. 4, ll. 33-42 of U.S. Pat. No. 7,631,065 B2 of Schweitzer et al.). The applicant's disclosure does not describe any such problem associated with previous systems. Thus, it appears that the language from the applicant's claims is merely describing the benefits of using generic, conventional distributed architecture, and is not taking the position that the claimed distributed architecture solves a technological problem of previous systems. In the absence of any discussion of the solving of a technological problem like the kind in Amdocs, the eligibility rationale from Amdocs is inapplicable to the applicant's claimed invention.
With respect to the claim rejections under 35 USC 103, the applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Such prior art includes the following:
U.S. Pat. No. 7,631,065 B2 to Schweitzer et al. discloses, "Importantly, the distributed data gathering, filtering and enhancements performed in the system 100 enables load distribution. Granular data can reside in the peripheries of the system 100, close to the information sources. This helps avoids reduce congestion in network bottlenecks but still allows the data to be accessible from a central location. In previous systems, all the network information flows to one location, making it very difficult to keep up with the massive record flows from the network devices and requiring huge databases" (see, e.g., col. 4, ll. 33-42).
Li, Zhi, et al. "Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors." Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 2018.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS Y. HO, whose telephone number is (571)270-7918. The examiner can normally be reached Monday through Friday, 9:30 AM to 5:30 PM Eastern.
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/THOMAS YIH HO/Primary Examiner, Art Unit 3624
1 In the Interview Summary of 05 June 2026, the examiner indicated that the claim rejection under 35 USC 101 would be withdrawn. Upon further consideration, the examiner has determined that the rejection should continue to be asserted. The examiner explains the reasoning for the rejection in the Response to Arguments section below.
