DETAILED ACTION
This action is in response to a filing filed claims on March 27th, 2025. Claims 1-10 is/are have been examined in this application.
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 .
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1: Claims 1-9 is/are drawn to method (i.e., a process) and Claims 10 is/are drawn to system (i.e., a manufacture), and. As such, claims 1-10 is/are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Representative Claim 1: A method for processing interactive message on a social platform, comprising:
obtaining data access permission of a target store, obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, and establishing a corresponding relationship between the keyword and the target product information according to a preset requirement;
establishing a connection with a social platform and monitoring a live broadcast status of the social platform, in response to the social platform performing live broadcasting, and obtaining an interactive message generated by live broadcast and a message generation time and a target user ID of the interactive message;
parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue;
grouping, within a preset time, the queue message according to the target user ID, and, in response to the grouped queue message comprising same contents, retaining the queue message with an earliest or a latest generation time;
and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, generating a transaction link according to the target product information, and sending the transaction link to a target user with a corresponding target user ID;
and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, and sending the transaction link to the target user with the corresponding target user ID, wherein the relevance is calculated based on a frequency of the keyword appearing in a sampled queue message.
(Examiner notes: The underlined claim terms above are interpreted as additional elements beyond the abstract idea and are further analyzed under Step 2A - Prong Two)
Under their broadest reasonable interpretation, the claim recites the abstract idea of facilitating a commercial transaction by obtaining product information, associating keywords with products, receiving and evaluating customer communications, identifying a product corresponding to the customer communications, and providing the customer with a transaction link for the identified product. More particularly, claim 1 recites obtaining information associated with a target store and its products, creating keyword-to-product relationships, receiving messages from users during a live sales presentation, determining whether the messages contain product-related keywords, organizing qualifying messages according to the corresponding users, eliminating repeated messages, evaluating the frequency or relevance of product-related keywords, selecting corresponding product information, and sending a link through which the user may conduct a transaction. These limitations, under their broadest reasonable interpretation, cover activities traditionally performed in commercial sales and marketing, such as reviewing customer inquiries, determining the product in which a customer has expressed interest, resolving ambiguous or repeated customer requests, recommending or selecting a product, and providing the customer with information through which the product may be purchased. Accordingly, the claim recites a process falling within the “certain methods of organizing human activity” grouping, including advertising, marketing or sales activities or behaviors and business relations.
Dependent claim 2 further narrows the abstract idea by specifying that the sampled messages are messages grouped according to a target-user identifier or messages associated with multiple user identifiers obtained within a preset period. These limitations further describe selecting and organizing customer communications according to the identity of the customers and the time during which the communications were received. Such grouping and sampling of customer information continues to fall within commercial sales activities and the mental processes of collecting, organizing, and evaluating information. Dependent claim 3 further narrows the claimed implementation by reciting a persistent connection between the social platform and the data-processing component and processing received messages according to a non-blocking input/output pattern. These limitations specify the computer-network environment and manner in which the customer communications are received and processed, but they do not change the underlying focus of the claim, which remains evaluating customer communications to determine corresponding products and facilitate sales transactions. The persistent connection and non-blocking input/output limitations are therefore more appropriately considered as additional computer-implementation elements when determining whether the abstract idea is integrated into a practical application. Dependent claim 4 further narrows the abstract idea by reciting monitoring a transaction link, determining whether the transaction link remains within a validity period when accessed, directing the customer to a product transaction page when the link remains valid, and returning an invalidation prompt when the link has expired. These limitations further describe administering access to a commercial transaction, evaluating whether a transaction opportunity remains available, and communicating the status of that transaction opportunity to the customer. Such limitations remain within commercial interactions involving sales activities and also involve collecting, evaluating, and communicating information. Dependent claim 5 further narrows the abstract idea by reciting monitoring a product transaction page, detecting that the customer has performed a payment operation, and locking a corresponding product quantity in inventory. These limitations further describe conventional commercial order-fulfillment and inventory-reservation activities, including recognizing payment and reserving merchandise for a paying customer. Such activities fall within commercial interactions, including sales, payment processing, inventory administration, and business relations. Dependent claim 6 further narrows the abstract idea by reciting obtaining the qualifying customer message, generating a temporary order based on corresponding product information, and generating the transaction link according to the temporary order. These limitations further describe preparing a preliminary customer order and providing the customer with a mechanism to complete the order. Preparing an order based on a customer request and providing instructions or information for completing the transaction are commercial sales activities and may also be performed through observations, evaluations, judgments, and instructions. Dependent claim 7 further narrows the claimed implementation by reciting loading a data-cache module and temporarily storing a connection identifier associated with the connection between the data-processing component and the social platform. These limitations specify generic computer storage and connection-management functions used in implementing the claimed sales process. They do not alter the underlying commercial focus of evaluating customer messages and providing corresponding transaction opportunities and are more appropriately evaluated as additional elements when determining whether the abstract idea is integrated into a practical application. Dependent claim 8 further narrows the claimed implementation by reciting storing the generated transaction link through the data-cache module and/or configuring a validity period for the transaction link. These limitations further describe storing transaction information and establishing a period during which the information may be used. Temporarily storing commercial transaction information and determining how long a transaction opportunity remains available are administrative and information-management activities that do not change the underlying focus on facilitating a product sale. Dependent claim 9 further narrows the abstract idea by reciting creating a message-introduction template, combining the template with the transaction link, and sending the combined message and link to the target social-platform user. These limitations further describe preparing and delivering an advertising, marketing, or sales communication that introduces a product or offer and provides access to the corresponding purchase opportunity. Such preparation, customization, and delivery of promotional content falls within commercial interactions, including advertising, marketing, and sales activities. The limitations also involve the mental processes of preparing information for communication, combining related information, selecting the intended recipient, and communicating the resulting promotional message
Independent claim(s) 10 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis.
As such, the Examiner concludes that claim 24 recites an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “addition element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The requirement to execute the claimed steps/functions using a social platform, a live broadcast, etc. (Claims 1 and 10) is/are equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Similarly, the limitations of applying a social platform, a live broadcast, etc. (Independent Claim(s) 1 and 10, and dependent claims 2-9) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to computerized environments (e.g., obtain, establish, parse, group, determine, etc. steps performed by a social platform, a live broadcast, etc.). This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
The recited additional element(s) of obtaining data-access permission for a target store, obtaining target-product information, creating an information-matching table that associates keywords with product information, establishing a connection with a social platform, monitoring a live-broadcast status, obtaining an interactive message together with its generation time and target-user identifier, parsing and matching the message against the information-matching table, conditionally adding matching messages to a message queue, excluding nonmatching messages from the queue, grouping queued messages by user identifier within a preset time, retaining an earliest- or latest-generated message when duplicate content is detected, determining the number of keywords in a queued message, calculating keyword relevance based on frequency, selecting a highest-relevance keyword, determining corresponding product information, generating a transaction link, and sending the transaction link to the corresponding user (Claim(s) 1 and 10), additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea). The recited additional element(s) do not meaningfully limit the claim because obtaining store-access information, product information, user-identifying information, message timestamps, and live-broadcast messages constitutes no more than pre-solution data gathering performed to obtain the information needed to carry out the abstract commercial activity. Establishing the social-platform connection and monitoring whether a live broadcast is occurring merely provide the technological environment and condition under which the information is collected. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. (See MPEP 2106.05(g)).
Dependent claim 2-9 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an "inventive concept." An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
As discussed above in “Step 2A – Prong 2”, the identified additional elements in independent claim(s) 1 and 10 and dependent claims 2-9 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
The recited additional element(s) of obtaining data-access permission for a target store, obtaining target-product information, creating an information-matching table that associates keywords with product information, establishing a connection with a social platform, monitoring a live-broadcast status, obtaining an interactive message together with its generation time and target-user identifier, parsing and matching the message against the information-matching table, conditionally adding matching messages to a message queue, excluding nonmatching messages from the queue, grouping queued messages by user identifier within a preset time, retaining an earliest- or latest-generated message when duplicate content is detected, determining the number of keywords in a queued message, calculating keyword relevance based on frequency, selecting a highest-relevance keyword, determining corresponding product information, generating a transaction link, and sending the transaction link to the corresponding user (Claim(s) 1 and 10), additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea) that is similar to “Receiving or transmitting data over a network, e.g., using the Internet to gather data”, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), “Storing and retrieving information in memory”, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; “Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price”, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here) (See MPEP 2106.05(d) (II)).
This conclusion is based on a factual determination. Applicant’s own published disclosure at paragraph [0020] acknowledges that “provides a system, the system is an e-commerce ERP system or an e-commerce platform system, and the system includes an order module or a social e-commerce module, and the order module or the social e-commerce module performs the operations in the method for processing interactive message on the social platform as described above ...” The applicant’s disclosure [0034], discloses the social platform is Facebook, and the corresponding commodity is broadcast live through Facebook. Other types of social platforms other than Facebook can also be applied. In order to push the corresponding commodity transaction link according to the interactive message (bullet message) of the social platform, the ERP system needs to be able to obtain the interactive message of the social platform, and then the cooperation of the data push module is required. Further, the data push module includes a connection component and a data processing component. The connection component establishes a persistent connection between the social platform and the data processing component. (i.e. conventional nature of receiving and transmitting data/messages over a network). This additional element therefore do not ensure the claim amounts to significantly more than the abstract idea.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
The dependent claims 2-9 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 1-10 are not eligible subject matter under 35 USC 101.
Claim Interpretation - 35 USC § 112 (Sixth Paragraph)
The following is a quotation of 35 U.S.C. § 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. § 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“Claim 10: A system for processing interactive message, comprising: an order module configured to execute operations in the method for processing interactive message of claim 1”
These limitations use generic placeholders, such as “module” and “engine,” as substitutes for “means,” and the placeholders are coupled with functional language, including “configured to,” “for,” “to,” “utilizing,” “leveraging,” “enabling,” and “employing.” The claim language does not recite sufficient structure, material, or acts for performing the recited functions, but instead recites the functions to be achieved.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph
Furthermore, the generic placeholder is not preceded by a structural modifier. The following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, paragraph 6: “mechanism for,” “module for,” “device for,” “unit for,” “component for,” “element for,” “member for,” “apparatus for,” “machine for,” or “system for.” See MPEP 2181.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claim(s) 10 has/have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: at applicant’s published specification paragraphs [0031], “FIG. 1 to FIG. 4, an embodiment of the present application provides a method for processing interactive message on a social platform, which is used in an order module or a social e-commerce module of an e-commerce enterprise resource planning (ERP) system or an e-commerce platform system …”
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim(s) 10 is/are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 10 recites the phrase “an order module configured to execute operations in the method for processing interactive message of claim 1”. The limitation is indefinite because it is unclear which operations of claim 1 are executed by the order module. Claim 1 recites multiple distinct operations directed to store-data access, product-information retrieval, keyword-table creation, social-platform connection and monitoring, message parsing and matching, queueing, grouping, duplicate-message retention, keyword counting, relevance calculation, product determination, transaction-link generation, and transmission of the transaction link. Claim 10 does not specify whether the order module performs all of these operations, only order-related operations, or only an unidentified subset of these operations. Therefore, one of ordinary skill in the art would not be reasonably apprised of the scope of the claimed order module.
As such, the claim is indefinite under 35 U.S.C. § 112, second paragraph, as it fails to distinctly point out and particularly claim the subject matter which the inventor regards as the invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status:
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2 and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. 20160092771 (“Buckley”) in view of U.S. Pub. 20200128286 (“Anders”) in view of U.S. Pub. 20220182709 (“Kruse”) in view of U.S. Pub. 20220141538 (“Li”) in view of U.S. Pub. 20100274857 (“Garza”).
As per claims 1, Buckley discloses, method for processing interactive message on a social platform, comprising (Examiner interprets that Buckley discloses client devices accessing a social-media service, posting social-media messages, and a server computer analyzing those messages) (“client devices 120 and 130 are associated with users of a social media service, and client device 140 is associated with server computer 150, which analyzes the social media service and messages posted to the social media service. Client devices 120 and 130 include respective instances of applications 122 and 132. In one embodiment, applications 122 and 132 are web browsers that users of client device 120 and 130 utilize to access a social media service and post messages (e.g., via user interfaces 124 and 134) on the social media service. In various embodiments, applications 122 and 132 include user profile information associated with users of client device 120 and 130 that utilize social media networks.”) (0015):
obtaining data access permission of a target store, obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets that Buckley’s product data includes product catalogs, products, descriptions, brands, prices, promotional messages, topics, and keywords. These are target-product information items and Buckley’s stored product-data dictionary containing topics and keywords associated with brands and products is reasonably interpreted as an information-matching table. A “table” broadly includes a dictionary, database mapping, or stored associative data structure) (“Product data 157 includes brand and product information. In one embodiment, product data 157 includes one or more product catalogs, which include a plurality of products and descriptions that correspond to the products and the brands associated with the products (e.g., a text description of a product or service, corresponding prices, etc.). For example, product data 157 includes product catalogs of multiple travel company brands, and the product catalogs for each brand provide text descriptions of products that the brand offers. In another embodiment, product data 157 includes promotional messages (e.g., brand and product advertising campaign messages) that can be sent as a social media message to users. In various embodiments, product data 157 includes topics and keywords that are associated with brands and products. For example, a travel company (i.e., a brand) offers cruises (i.e., a product), and product data 157 includes a dictionary of associated topics and keywords that include “ski, skiing, mountain, and slopes”) (0024),
and establishing a corresponding relationship between the keyword and the target product information according to a preset requirement (Examiner interprets that Buckley’s product-related keywords and topics associates with product-catalog information. Its predefined topic model and matrix-factorization rules constitute the preset requirements governing the keyword-product relationship) (“product catalog for a brand or company advertises products in a product catalog, and rather than represent a product catalog as many individual entities, product data 157 includes data that summarizes the product catalog by a small number of topics that captures the overall product information contained in the catalog. For example, the topics are determined utilizing topic modeling in machine learning. The topics, instead of the products themselves, can be related and compared to the social media messages (e.g., of social media data 156) and the brands for which there is high similarity to one of the brand's topics are recommended … server computer 150 utilizes a matrix factorization topic model, which is included in analysis models 152. The model denotes N as the number of features (i.e., the number of distinct relevant words in the product catalog), M as the number of products in the catalog, and K as the number of topics intended for output. Let X be a matrix of word counts representing a product catalog where X.sub.ij is the number of time word j appears in the description of product i. In this example embodiment, the assumption is that each product description can be summarized by K topics, where K is much smaller than the number of words in the dictionary. This means that rather than represent a product by N word counts (assuming a dictionary with N words), the product description can be represented by K topic coefficients. Next, the model utilizes an optimization problem that utilizes a matrix factorization representation of a product catalog, which is motivated by the assumption that the catalog information, input as a matrix with N numbers for each product, can be represented by the product of two matrices with far fewer parameters”) (0026-0027, 0024);
establishing a connection with a social platform and monitoring a live broadcast status of the social platform (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets that Buckley captures live social-media messages from one or more social networks and associates those messages with social-media accounts, necessarily establishing communication with the social platform) (“Social media data 156 includes social media messages and data that is associated with the social media messages. In various examples, the social media messages can be from a single social media network or from a plurality of social media networks. In additional examples, the social media messages can be any form of messaging utilized in a social media network (e.g., messages directed to or from one user to one or more additional users, messages posted to an activity stream on a social media network, etc.). In one embodiment, server computer 150 stores social medial messages in social media data 156 utilizing stream data processing. For example, server computer 150 captures live social media messages in real-time and periodically stores the social media messages in social media data 156 … social media data 156 includes user profile information associated with social media messages. For example, if a user of client device 120 posts a social media message that server computer 150 stores in social media data 156, then social media data 156 associates the social media message to the user profile of the user of client device 120. The associated user profiles can includes additional social media messages posted by the user, an association to the social media account of the user that can receive messages (e.g., targeted promotional messages from server computer 150), an additional user profiles that are associated with the user profile (e.g., friends of the user, etc.). In another embodiment, social media data 156 includes metadata associated with social media messages. For example, the metadata can include data that a social media message was posted, user profiles references in a social media message, the location that the user posted the social media message, etc. In another example, social media data 156 includes metadata of context information associated with social media messages, which can be determined by message analysis program 200 and analysis models 152”) (0022-0024, 0026-0027),
parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table (Examiner interprets that Buckley uses AQL text extractors, dictionary matches, regular-expression searches, and rule-based text analytics to parse unstructured social-media messages and Buckley matches extracted message text against stored product topics and keywords) (“Social media messages can be composed of unstructured text, which is unlike text in traditional articles such as news articles, as unstructured text does not conform to conventional grammar rules. Wide-spread use of abbreviations (e.g., “want 2 c” instead of “want to see”) can make the task of extraction and normalization complex. In order to address this problem, message analysis program 200 utilizes text extractors that provide rule-based text analytics languages (e.g., Annotation Query Language (AQL), Sequential Query Language (SQL), etc.). The basic building block of AQL is a view, which may be created via dictionary matches, regular expression searches, and combinations of other views. Message analysis program 200, utilizing AQL, can also provide functionality for efficient compilation and computation (using Map-Reduce) of the rules. Many queries that message analysis program 200 utilizes in the text extractors are fairly simple. For example, social media messages from users that like to travel can be identified simply by searching for a few phrases, such as “likes to travel” and “traveler” in a description field of a user profile. In other examples, the text extractors can be much more complex and require thousands of lines of AQL to build the desired rules … message analysis program 200 identifies social media messages that include information related to the brand. In one embodiment, message analysis program 200 identifies social media messages that include information related to the brand (i.e., the brand from step 202) based on the analysis of social media messages in step 206. In example embodiments, message analysis program 200 identifies social media messages from social media data 156 (filtered in step 204) that include text of the topics and keywords associated with the brand (from step 202). In another example embodiment, message analysis program 200 identifies social media messages that include information related to the brand in response to a user selection (e.g., via a user of client device 140 utilizing web dashboard 145). For example, message analysis program 200 receives a selection of a keyword in keyword listing 310 of example web dashboard 300”) (0035-0039),
adding the interactive message to a message queue to form a queue message (Examiner interprets that Buckley’s text extractors leave a retained set of matching messages, which are then processed one-by-one for downstream ranking) (“Message analysis program 200 utilizes the text extractors (described above) as an initial filter to locate social media messages and corresponding users. Message analysis program 200 uses the text extractors first because the text extractors can be designed to look for particular patterns, locations, etc. and can detect the context (such as whether a user intends to buy something) of social media messages. In example embodiments, message analysis program 200 first passes all social media messages through the text extractors, which leaves a set of messages that exhibit intent and context … Message analysis program 200 inputs the filtered social media messages and, one-by-one, ranks the brand-specific topics according to which topics are most similar to each social media message. Message analysis program 200 utilizes topics, which are distributions over the words. In the following model, denote h as a vector holding the distribution of a topic, with each component being the probability of a word given the topic, and let x be a vector holding the word count of a social media message. One of the main issues with understanding social media messages is that the language used by people on a social media network can be very different from the language used in product catalogs, namely the bag-of-words used to build x is very different from the bag-of-words used to describe topics. Let N be the number of distinct words in the message”) (0041-0042, 0019),
and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue (Examiner interprets that Buckley filters out messages that do not have the appropriate context) (“message analysis program 200 filters social media messages based on context. In one embodiment, message analysis program 200 filters social media messages stored in social media data 156 based on the context of the social media messages. Message analysis program 200 utilizes text extractors, which include rules for analyzing text. In an example embodiment, message analysis program 200 utilizes text extractors to analyze social media messages to determine the intent of messages, which provides an indication of whether the message describes a past experience or a desired experience. In another example embodiment, message analysis program 200 filters out social media messages that include promotional content or other non-authentic social media user messages. Message analysis program 200 filters the social media messages that are stored in social media data 156 to identify, and subsequently utilize, social media messages with an appropriate context”) (0033);
and determining a number of keywords in the queue message (Examiner interprets that Buckley represents message text as a word-count vector over a product-related dictionary. Each nonzero vector position identifies a matching keyword. Counting those nonzero positions determines whether one or multiple different keywords are present) (“product search program 200 captures descriptive metadata of audio/video source 120 (e.g., descriptive metadata from a live stream), stores the descriptive metadata in storage device 142, and uses the descriptive metadata to identify products in audio/video source 120. For example, product search program 200 stores data that includes physical attributes of objects (e.g., a set of glasses with grey frames and orange lens) to identify products in a product review of a blogger from a social media broadcast. In yet another embodiment, product search program 200 captures descriptive metadata of audio/video source 120, stores the descriptive metadata in storage device 142, and uses the descriptive metadata to return products that correspond with the descriptive metadata. For example, product search program 200 stores data that includes physical attributes of objects (e.g., a set of glasses with grey frames and orange lens) to return products that share similar physical attributes”) (0025, 0040-0042),
in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, generating a transaction link according to the target product information, and sending the transaction link to a target user with a corresponding target user ID (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets that Buckley stores keyword-product associations. When only one product keyword is present, the sole matching association identifies the corresponding product information without further ranking) (“product search program 200 captures metadata associated with a live streaming event. In various embodiments of the present invention, metadata is included or associated with streaming media of a live streaming event to identify and return products. In one embodiment, product search program 200 stores metadata that is in a live streaming event of audio/video source 120 in storage device 142. For example, product search program 200 stores audio and video data that includes structural and descriptive metadata of a product review of a blogger from a social media broadcast in memory of a server” and “product search program 200 adds the selected product to a list corresponding to a user. More specifically, in response to determining that the user chooses to purchase a product of the presented product list (of step 210) in the future (decision step 214, “NO” branch), product search program 200 adds the selected product to a list corresponding to the user (step 216). For example, when product search program 200 determines that the user chooses to purchase the set of grey glasses with orange lenses in the future, then product search program 200 adds the set of grey glasses with orange lenses to a list corresponding to the user (step 216). In various embodiments, the list corresponding to the user is a wish list (e.g., future shopping cart) or another type of list associated with the user (i.e., defined and/or indicated through user preferences)”) (0024 and 0039),
and, in response to that the queue message comprising multiple keywords (Examiner interprets that Buckley calculates relevance or correlation between social-media messages and product-related topics using message word counts) (“message analysis program 200 determines relevancy scores for the identified social media messages. In one embodiment, message analysis program 200 determines relevancy scores for the social media messages identified in step 208. A relevancy score provides an indication of the likelihood that a social media message is related to a particular brand or product. In various embodiments, message analysis program 200 determines a relevancy score that indicates a correlation between social media messages and products of a brand … Message analysis program 200 inputs the filtered social media messages and, one-by-one, ranks the brand-specific topics according to which topics are most similar to each social media message. Message analysis program 200 utilizes topics, which are distributions over the words. In the following model, denote h as a vector holding the distribution of a topic, with each component being the probability of a word given the topic, and let x be a vector holding the word count of a social media message. One of the main issues with understanding social media messages is that the language used by people on a social media network can be very different from the language used in product catalogs, namely the bag-of-words used to build x is very different from the bag-of-words used to describe topics. Let N be the number of distinct words in the message. Message analysis program 200 extends topic h such that the topic has a probability for every word in x; for each word that does not appear in the topic distribution, we assign a default minimum probability”) (0041-0042),
calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance (Examiner interprets that Buckley ranks brand-specific topics according to similarity to each message and displays product keywords according to their frequency in scored social-media messages. Selecting the top-ranked or most frequently represented keyword is the predictable output when one product response must be generated) (“Web dashboard is a web-based GUI that a user of client device 140 can utilize to browse the scored social media messages and to engage relevant users. Web dashboard 145 is also capable of providing insights into the popularity of travel destinations and keywords, as well as overall social media activity. FIG. 3 depicts example web dashboard 300, which includes travel brands 305, keyword listing 310, message recommendations 315, and promotional messages 320. Travel brands 305 is a selection of travel brands that a user of example web dashboard 300 can select and utilize to analyze social media messages. Keyword listing 310 displays keywords associated with a selected brand, which vary in size depending on the frequency of the keyword in scored social media messages”) (0017, 0040-0042),
determining corresponding target product information based on the corresponding relationship of the keyword (Examiner interprets that Buckley displays keywords with prominence varying according to their frequency in scored social-media messages and calculates relevance using word-count vectors and the highest-ranked keyword is applied to Buckley’s stored keyword/product association to identify the corresponding product information) (“product data 157 includes brand and product information. In one embodiment, product data 157 includes one or more product catalogs, which include a plurality of products and descriptions that correspond to the products and the brands associated with the products (e.g., a text description of a product or service, corresponding prices, etc.). For example, product data 157 includes product catalogs of multiple travel company brands, and the product catalogs for each brand provide text descriptions of products that the brand offers. In another embodiment, product data 157 includes promotional messages (e.g., brand and product advertising campaign messages) that can be sent as a social media message to users. In various embodiments, product data 157 includes topics and keywords that are associated with brands and products” and “message analysis program 200 identifies social media messages that include information related to the brand. In one embodiment, message analysis program 200 identifies social media messages that include information related to the brand (i.e., the brand from step 202) based on the analysis of social media messages in step 206. In example embodiments, message analysis program 200 identifies social media messages from social media data 156 (filtered in step 204) that include text of the topics and keywords associated with the brand (from step 202). In another example embodiment, message analysis program 200 identifies social media messages that include information related to the brand in response to a user selection (e.g., via a user of client device 140 utilizing web dashboard 145). For example, message analysis program 200 receives a selection of a keyword in keyword listing 310 of example web dashboard 300. In response, message analysis program 200 identifies social media messages that include text that relates to the selected keyword”) (0024 and 0039).
Buckley specifically doesn’t disclose, obtaining data access permission of a target store, generating a transaction link according to the target product information, and sending the transaction link to a target user with a corresponding target user ID, generating a transaction link according to the target product information, and sending the transaction link to the target user with the corresponding target user ID, wherein the relevance is calculated based on a frequency of the keyword appearing in a sampled queue message, however Anders discloses, obtaining data access permission of a target store (Examiner interprets that Anders permits the broadcaster to define a specific website or inventory source from which products are retrieved. Under a broad interpretation, configuring the system to access the broadcaster-selected store source constitutes obtaining permission to access that target-store product data. This remains an inferential limitation rather than an express credential or token disclosure) (“present invention recognize that the product search can be defined by user preferences. Thus, various embodiments of the present invention can derive products from a predefined selective site or through a wider search with a broad result returned. For example, a blogger is live streaming a product review and predefines that the search results for products are limited to a specific website (e.g., a website associated with the blogger). Alternatively, a blogger is live streaming a product review and does not define a limitation, allowing product results to be returned from all online stores”) (0010, 0034),
and monitoring a live broadcast status of the social platform (Examiner interprets that Anders initiates product-search operations during a live-stream event and monitors the user’s interaction with the live-streaming broadcast. The system’s operation depends on determining that the live-stream event is active) (“a user interaction occurs when an act of a user effects the live streaming event. In one embodiment, product search program 200 monitors user interface 132 of client device 130 to determine a user interaction with a live streaming event occurring on application 134. For example, product search program 200 monitors a user interface of a mobile device and detects a user interaction of the user clicking a “like” button during a product review of a blogger on a social media broadcast occurring on a web browser (e.g., application 134). Accordingly, product search program 200 determines that a user interaction with the live streaming event is occurring …”) (0027-0028),
generating a transaction link according to the target product information (Examiner interprets that Anders retrieves the corresponding product listing from a defined online-store inventory. A selectable online-shopping product listing is reasonably interpreted as a transaction link because it electronically directs the user to the corresponding purchasable product) (“product search program 200 retrieves products from a defined set of sources (e.g., determined by preferences of a user). In one embodiment, product search program 200 returns products from a user defined source. For example, a broadcaster defines preferences that product search program 200 returns products from an inventory listing of a website of the broadcaster. In another example, a broadcaster defines a preference that product search program 200 can return products from an inventory listing of any site available over the Internet (e.g., a default preference). In another embodiment, a user defines a set of preferences that prioritizes sources from which product search program 200 returns products. For example, a broadcaster defines preferences that product search program 200 returns products from a listing of a website of the broadcaster before returning products from inventories of all sites available over the Internet”) (0034),
and sending the transaction link to a target user with a corresponding target user ID (Examiner interprets that Anders returns the product listing to the application on which the interacting user is viewing the live stream) (“in response to the user interaction, product search program 200 presents a product or list of products to the user viewing the live streaming event on application 134. In one embodiment, product search program 200 returns products over network 110 to application 134 of client device 130 where the user is viewing the live streaming event of audio/video source 120. For example, product search program 200 returns a set of grey glasses with orange lenses to a web browser of a tablet where the user is viewing the product review of the blogger from a social media broadcast. Additionally, product search program 200 displays the list of products returned in a dialogue window while the user continues to view the product review. In another example, product search program 200 returns a set of grey glasses with orange lenses to a web browser and displays the set of grey glasses with orange lenses in a frame within the same window of the web browser as the product review of the blogger”) (0035);
generating a transaction link according to the target product information, and sending the transaction link to the target user with the corresponding target user ID, wherein the relevance is calculated based on a frequency of the keyword appearing in a sampled queue message (Examiner interprets that Anders retrieves the corresponding online-shopping product listing and returns it over the network to the application used by the interacting live-stream viewer) (“product search program 200 retrieves products from a defined set of sources (e.g., determined by preferences of a user). In one embodiment, product search program 200 returns products from a user defined source. For example, a broadcaster defines preferences that product search program 200 returns products from an inventory listing of a website of the broadcaster. In another example, a broadcaster defines a preference that product search program 200 can return products from an inventory listing of any site available over the Internet (e.g., a default preference). In another embodiment, a user defines a set of preferences that prioritizes sources from which product search program 200 returns products. For example, a broadcaster defines preferences that product search program 200 returns products from a listing of a website of the broadcaster before returning products from inventories of all sites available over the Internet … n response to the user interaction, product search program 200 presents a product or list of products to the user viewing the live streaming event on application 134. In one embodiment, product search program 200 returns products over network 110 to application 134 of client device 130 where the user is viewing the live streaming event of audio/video source 120. For example, product search program 200 returns a set of grey glasses with orange lenses to a web browser of a tablet where the user is viewing the product review of the blogger from a social media broadcast. Additionally, product search program 200 displays the list of products returned in a dialogue window while the user continues to view the product review. In another example, product search program 200 returns a set of grey glasses with orange lenses to a web browser and displays the set of grey glasses with orange lenses in a frame within the same window of the web browser as the product review of the blogger”) (0034-0035).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, establishing a connection with a social platform and monitoring a live broadcast status of the social platform, parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue, and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, as disclosed by Buckley, obtaining data access permission of a target store, generating a transaction link according to the target product information, and sending the transaction link to a target user with a corresponding target user ID, generating a transaction link according to the target product information, and sending the transaction link to the target user with the corresponding target user ID, wherein the relevance is calculated based on a frequency of the keyword appearing in a sampled queue message, as taught by Anders in response to incorporate product-listing technique that would permit those users to access and purchase the identified products directly, while reducing separate product-search and navigation steps.
Buckley specifically doesn’t disclose, in response to the social platform performing live broadcasting, and obtaining an interactive message generated by live broadcast and a message generation time and a target user ID of the interactive message, however Kruse discloses, in response to the social platform performing live broadcasting, and obtaining an interactive message generated by live broadcast and a message generation time and a target user ID of the interactive message (Examiner interprets that Kruse receives customer comments from interactive video-player applications while the live-video sale is occurring. A comment matching the purchase template triggers a purchase order) (“product search program 200 determines that the live streaming event has not ended (decision step 220, “NO” branch), then product search program 200 returns to step 202 to capture metadata associated with the live streaming event. For example, if product search program 200 determines that the product review of the blogger from a social media broadcast has not ended, then product search program 200 returns to step 202 to capture and store structural and descriptive metadata of a product review of a blogger from a social media broadcast in memory of the server … Response processing system 130 obtains, standardizes, aggregates, and processes the customer responses from all of the video player applications. Responses from different video player applications or other tools may be transformed to a common format for analysis and further processing. Each response may be tagged with data such as the identifier of the person responding, the site or app from which the response was generated, and a timestamp of when the response was generated. Responses may be processed to generate sales transactions, which result in updates to orders, inventory, and backlog data in database 100. They may also be aggregated, filtered, tagged, sorted, categorized, or otherwise processed, and forwarded to a response viewing feature 117 in the sales administration system 110. User 111 may then view the responses from users, potentially in almost real time, to gauge the reaction to the video stream and to adjust the sales process accordingly. The response viewing feature 117 may also be available to the video production staff performing capture 141 so that video content may be adjusted as desired based on incoming responses.”) (0042-0044).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, establishing a connection with a social platform and monitoring a live broadcast status of the social platform, parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue, and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, as disclosed by Buckley, in response to the social platform performing live broadcasting, and obtaining an interactive message generated by live broadcast and a message generation time and a target user ID of the interactive message, as taught by Kruse in response to obtains comments generated during live-video sales, associates each comment with a customer identifier and generation timestamp, and converts template-matching comments into purchase transactions to allow free-form product-related social comments to be analyzed and converted into product-purchase opportunities during a live broadcast.
Buckley specifically doesn’t disclose, and in response to the grouped queue message comprising same contents, however Li discloses, grouping, within a preset time, the queue message according to the target user ID, and in response to the grouped queue message comprising same contents (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets that Li creates a parent node associated with a first request from a viewer-user account and links later requests from the same viewer account as child nodes. The linked nodes constitute a group associated with one target-user ID) (“Order Compilation Engine detects a first request sent from a requesting user account during a first session associated with a target user account, whereby the target account may be associated with multiple sessions accessible by the requesting user account and multiple other user accounts. The Order Compilation Engine generates a parent node associated with the first request to be active for a range of time selected by the target user account. The range of time initiates based on generation of the parent node. The Order Compilation Engine detects a subsequent second request sent from the requesting user account to the target user account and generates a node associated with subsequent second request. The Order Compilation Engine verifies that the subsequent second request is not an initial request by the requesting user account during the range of time. The Order Compilation Engine further verifies that the subsequent second request has been detected prior to expiration of the range of time. Upon verification, The Order Compilation Engine identifies (i.e. labels, defines, flags) the node as a child node by generating a link to connect the parent node and the child node. The Order Compilation Engine may further generate child nodes representing additional subsequent request from the requesting user account detected during the range of time. Each additional child node may be linked to the same parent node as well … The second request may be detected during the first live video stream or during a second live video stream from the host user account that began after termination of the first live video stream. The O.C. Engine generates a node for the second request. The O.C. Engine verifies the second request is not the viewer user account's initial request sent to the host user account and that the range of time has not expired. For example, the O.C. Engine may detect that an active parent node for the viewer user account corresponds with the host user account. Upon verification, the O.C. Engine identifies the second request's node as a child node (i.e. subordinated node) by linking the child node to the parent node (i.e. primary node). When the range of time expires, the O.C. Engine does not allow for additional requests from the viewer user account to trigger generation of more child node's to be linked to the parent node”) (0004-0006, 0044, claim 1),
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, establishing a connection with a social platform and monitoring a live broadcast status of the social platform, parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue, and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, as disclosed by Buckley, grouping, within a preset time, the queue message according to the target user ID, as taught by Li in response to incorporate linking an initial viewer request and subsequent requests from that same viewer during an active time period to consolidate related comments from one customer, prevent the comments from being treated as unrelated transactions, and permit a single coordinated product response.
Buckley specifically doesn’t disclose, and, in response to the grouped queue message comprising same contents, retaining the queue message with an earliest or a latest generation time, however Garza discloses, and in response to the grouped queue message comprising same contents (Examiner interprets that Garza calculates content-integrity values, including checksums or CRC values, and compares stored message content with incoming message content to determine whether an entire payload or relevant content pattern is duplicated) (“automated duplicate message content detection described herein, received message content is stored. Storage of received message content may be performed by calculating a message content integrity value based upon the message content associated with a received message. The message content integrity value may include a calculated checksum value, a calculated cyclical redundancy check (CRC) value, or any other form of message content integrity value. The message content associated with the received message may then be stored in a memory, such as a cache memory, at a location referenced or indexed by the calculated message content integrity value … Determining whether message content is duplicated within subsequent messages may include determining whether an entire message payload area is duplicated (e.g., identical). Determining whether message content is duplicated within subsequent messages may also include determining whether a specific data pattern is present within the stored message content and within the message content associated with an incoming message or portion of an incoming message. Continuing with the XSLT example above, rules may be constructed to manipulate message content upon extraction of the message content from an incoming message. In such a situation, a repeating pattern may be identified by a comparison rule encoded within the XSLT. Many other options exist for determining whether message content within multiple messages is duplicated and all are considered”) (0024-0025),
retaining the queue message with an earliest or a latest generation time (Examiner interprets that Garza permits the system to discard the newly received duplicate while retaining the previously stored content) (“as part of the example duplicate message content management action processing, the process 400 makes a determination at decision point 426 as to whether to discard the stored message content (e.g., the older message content). When a determination is made to discard the stored message content, the process 400 stores the new incoming message content at the memory index referenced by the calculated message content integrity value at block 428. Upon storing the new incoming message content or upon determining not to discard the stored message content, the process 400 makes a determination at decision point 430 as to whether to discard the new incoming message content. When a determination is made to discard the new incoming message content, the process 400 discards the new incoming message content at block 432. Upon discarding the new incoming message content or upon determining not to discard the new incoming message content, the process 400 makes a determination at decision point 434 as to whether to generate a duplicate message content report. The duplicate message content report may be generated and sent to a device monitored or controlled by a system administrator, report message logging system, or any other suitable message reporting device or system. Upon determining to report the duplicate message content, the process 400 reports the duplicate message content to the selected destination at block 436. Upon reporting the duplicate message content or upon determining not to report the duplicate message content, the process 400 returns to decision point 402 to iterate between decision point 402 and decision point 404 as described above”) (0070).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, establishing a connection with a social platform and monitoring a live broadcast status of the social platform, parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue, and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, as disclosed by Buckley, and, in response to the grouped queue message comprising same contents, retaining the queue message with an earliest or a latest generation time, as taught by Garza in response to apply duplicate-content detection and retain-old or retain-new rules thus discarding the new duplicate would retain the earliest-generated message, while replacing the stored message with the new duplicate would retain the latest-generated message, further preventing redundant processing.
As per claims 2, Buckley specifically doesn’t disclose, wherein the sampled queue message is a queue message after grouping based on the target user ID or a queue message comprising multiple different target user IDs obtained based on a preset time, however Li discloses, wherein the sampled queue message is a queue message after grouping based on the target user ID or a queue message comprising multiple different target user IDs obtained based on a preset time (Examiner interprets that Li creates a parent node for a first request from a viewer-user account and links subsequent requests from that same viewer during an active time range. The linked requests are a sampled set of messages after grouping by target-user ID) (“Order Compilation Engine detects a first request sent from a requesting user account during a first session associated with a target user account, whereby the target account may be associated with multiple sessions accessible by the requesting user account and multiple other user accounts. The Order Compilation Engine generates a parent node associated with the first request to be active for a range of time selected by the target user account. The range of time initiates based on generation of the parent node. The Order Compilation Engine detects a subsequent second request sent from the requesting user account to the target user account and generates a node associated with subsequent second request. The Order Compilation Engine verifies that the subsequent second request is not an initial request by the requesting user account during the range of time. The Order Compilation Engine further verifies that the subsequent second request has been detected prior to expiration of the range of time. Upon verification, The Order Compilation Engine identifies (i.e. labels, defines, flags) the node as a child node by generating a link to connect the parent node and the child node. The Order Compilation Engine may further generate child nodes representing additional subsequent request from the requesting user account detected during the range of time. Each additional child node may be linked to the same parent node as well … The second request may be detected during the first live video stream or during a second live video stream from the host user account that began after termination of the first live video stream. The O.C. Engine generates a node for the second request. The O.C. Engine verifies the second request is not the viewer user account's initial request sent to the host user account and that the range of time has not expired. For example, the O.C. Engine may detect that an active parent node for the viewer user account corresponds with the host user account. Upon verification, the O.C. Engine identifies the second request's node as a child node (i.e. subordinated node) by linking the child node to the parent node (i.e. primary node). When the range of time expires, the O.C. Engine does not allow for additional requests from the viewer user account to trigger generation of more child node's to be linked to the parent node”) (0004-0006, 0044, claim 1).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, establishing a connection with a social platform and monitoring a live broadcast status of the social platform, parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue, and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, as disclosed by Buckley, wherein the sampled queue message is a queue message after grouping based on the target user ID or a queue message comprising multiple different target user IDs obtained based on a preset time, as taught by Li in response to incorporate keyword-frequency and relevance analysis for finite group of messages from one viewer because the grouping isolates messages attributable to that viewer and improves the accuracy of selecting the product most relevant to that viewer.
As per claims 9, Buckley discloses, creating a message introduction template, merging the message introduction template with the transaction link (Examiner interprets that Buckley stores and displays promotional campaign messages associated with brands and products. Its dashboard lists promotional campaign messages that may be sent to social-media users. Buckley permits selection of a promotional campaign message or use of a default promotional campaign message associated with the applicable brand. A stored, reusable, product- or brand-associated promotional message is reasonably interpreted as the claimed message-introduction template) (“product data 157 includes promotional messages (e.g., brand and product advertising campaign messages) that can be sent as a social media message to users. In various embodiments, product data 157 includes topics and keywords that are associated with brands and products …” and “message analysis program 200 receives a selection of one or more social media messages with a relevancy score that meets or exceeds the threshold condition, and message analysis program 200 identifies the corresponding users. In an additional embodiment, message analysis program 200 can receive a selection of a promotional campaign message to send to the selected users. In an example embodiment, message analysis program 200 receives a selection of one or more users displayed in message recommendations 315 in example web dashboard 300. Additionally, message analysis program 200 receives a selection of a promotional campaign message in promotional messages … receiving a selection of a topic or keyword in keyword listing 310, message analysis program 200 displays social media messages (and related data) that are related to the selected topic or keyword in message recommendations 315. Message analysis program 200 receives a selection of one or more of the social media users (or corresponding social media messages) in message recommendations 315 (step 214) and receives a selection of a promotional campaign message in promotional messages 320. In response to receiving the selection of one or more users and a promotional campaign message, message analysis program 200 sends the selected promotional campaign message to the one or more selected users”) (0024 and 0044-0047, 0017).
Buckley specifically doesn’t disclose, and sending the merged transaction link to a target user of the social platform, however Anders discloses, and sending the merged transaction link to a target user of the social platform (Examiner interprets that Anders determines an online-shopping product listing corresponding to the identified product and presents an option to purchase that product. The examiner interprets the selectable online product listing or shopping-cart resource as the claimed transaction link) (“a user interaction occurs when an act of a user effects the live streaming event. In one embodiment, product search program 200 monitors user interface 132 of client device 130 to determine a user interaction with a live streaming event occurring on application 134. For example, product search program 200 monitors a user interface of a mobile device and detects a user interaction of the user clicking a “like” button during a product review of a blogger on a social media broadcast occurring on a web browser (e.g., application 134). Accordingly, product search program 200 determines that a user interaction with the live streaming event is occurring …”) (0027-0028, 0045).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, establishing a connection with a social platform and monitoring a live broadcast status of the social platform, parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue, and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, as disclosed by Buckley, and sending the merged transaction link to a target user of the social platform, as taught by Anders in response to incorporate product-listing technique that would permit those users to access and purchase the identified products directly, while reducing separate product-search and navigation steps.
Claims 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. 20160092771 (“Buckley”) in view of U.S. Pub. 20200128286 (“Anders”) in view of U.S. Pub. 20220182709 (“Kruse”) in view of U.S. Pub. 20220141538 (“Li”) in view of U.S. Pub. 20100274857 (“Garza”) in view of NPL “Cutting Tail Latency in Data Center Networks with Flow Replication” (“Liu”).
As per claims 3, Buckley specifically doesn’t disclose, establishing a persistent connection between the social platform, however Kruse discloses, establishing a persistent connection between the social platform and a data processing component through a connection component, wherein the data processing component processes the received interactive message based on a non-blocking I/O pattern (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets that Kruse continuously transmits a live-video stream to multiple interactive applications while receiving, standardizing, aggregating, and processing responses from those applications) (“modified video stream 118 may be streamed to a video distribution system 120, which transmits the modified stream over one or more networks to one or more interactive video player applications. These applications may be used by or viewed by customers, who may then generate responses to the video including purchases of displayed products. An interactive video player application may be any website, application, mobile app, client, server, or system that displays the modified video stream 118 to one or more viewers. In some embodiments the video player application may provide a commenting feature for users to respond to the video. In other embodiments users may respond to a video using other tools or features, such as texting or email. An interactive video player application may be for example a web page with a frame that displays the video stream. For example, live video streams may be accessed by users on Facebook® pages, on YouTube®, or on similar social media sites. An interactive video player application may be a mobile app, such as an app associated with the merchant or with a group of merchants, that shows the video on a mobile device (such as a phone or tablet) and that allows the user to respond … product search program 200 determines that the live streaming event has not ended (decision step 220, “NO” branch), then product search program 200 returns to step 202 to capture metadata associated with the live streaming event. For example, if product search program 200 determines that the product review of the blogger from a social media broadcast has not ended, then product search program 200 returns to step 202 to capture and store structural and descriptive metadata of a product review of a blogger from a social media broadcast in memory of the server … Response processing system 130 obtains, standardizes, aggregates, and processes the customer responses from all of the video player applications. Responses from different video player applications or other tools may be transformed to a common format for analysis and further processing. Each response may be tagged with data such as the identifier of the person responding, the site or app from which the response was generated, and a timestamp of when the response was generated. Responses may be processed to generate sales transactions, which result in updates to orders, inventory, and backlog data in database 100. They may also be aggregated, filtered, tagged, sorted, categorized, or otherwise processed, and forwarded to a response viewing feature 117 in the sales administration system 110. User 111 may then view the responses from users, potentially in almost real time, to gauge the reaction to the video stream and to adjust the sales process accordingly. The response viewing feature 117 may also be available to the video production staff performing capture 141 so that video content may be adjusted as desired based on incoming responses.”) (0039-0044).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, establishing a connection with a social platform and monitoring a live broadcast status of the social platform, parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue, and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, as disclosed by Buckley, in response to the social platform performing live broadcasting, and obtaining an interactive message generated by live broadcast and a message generation time and a target user ID of the interactive message, as taught by Kruse in response to obtains comments generated during live-video sales, associates each comment with a customer identifier and generation timestamp, and converts template-matching comments into purchase transactions to allow free-form product-related social comments to be analyzed and converted into product-purchase opportunities during a live broadcast.
Buckley specifically doesn’t disclose, and a data processing component through a connection component, wherein the data processing component processes the received interactive message based on a non-blocking I/O pattern, however Liu discloses, and a data processing component through a connection component, wherein the data processing component processes the received interactive message based on a non-blocking I/O pattern (Examiner interprets that RepNet implements a server-side network-processing component using Node.js. The component listens for, receives, and processes data over network connections. RepNet expressly teaches a Node.js server using a single-threaded event loop, non-blocking sockets, asynchronous network methods, and callbacks to process data received over multiple concurrent connections) (Pgs. 3-4).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, establishing a connection with a social platform and monitoring a live broadcast status of the social platform, parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue, and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, as disclosed by Buckley, and a data processing component through a connection component, wherein the data processing component processes the received interactive message based on a non-blocking I/O pattern, as taught by Liu in response to apply the Node.js’s event-driven and non-blocking socket architecture to allow the system to continue receiving and processing other live comments while a particular message-related I/O operation remains incomplete, thereby reducing blocking delays, thread-switching overhead, and processing latency during periods of high concurrent message volume.
Claims 4-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. 20160092771 (“Buckley”) in view of U.S. Pub. 20200128286 (“Anders”) in view of U.S. Pub. 20220182709 (“Kruse”) in view of U.S. Pub. 20220141538 (“Li”) in view of U.S. Pub. 20100274857 (“Garza”) in view of NPL “Cutting Tail Latency in Data Center Networks with Flow Replication” (“Liu”) in view of U.S. Pub. 20120029691 (“Mockus”).
As per claims 4, Buckley specifically doesn’t disclose, monitoring the transaction link, and, in response to detecting that the target user of the social platform accesses the corresponding transaction link, determining whether the transaction link is within a validity period; in response to determining that the transaction link is within the validity period, redirecting the target user to a corresponding product transaction page according to the transaction link; and in response to determining that the transaction link exceeds the validity period, returning a transaction link invalidation prompt to the target user, however Mockus discloses, monitoring the transaction link (Examiner interprets that Mockus stores transaction information associated with a purchase code in a central database and evaluates the code when the user submits it. Monitoring access to the transaction link corresponds to receiving the transaction identifier carried by or associated with the accessed link and looking up its stored transaction record) (“method of retrieving products from a vending machine or automated retail store by entering in a code that is verified on a central server before dispensing the products. Purchases made through a mobile device are linked to a specific machine to which purchased merchandise can be received. This information is stored in a data store on the central server (FIG. 13) and then the information is synchronized with the machines in the field. This places a temporary hold on the merchandise so it cannot be sold to another consumer. After the set time, the purchase code expires, the hold on the merchandise is lifted and the merchandise becomes available again. The user may apply for a credit or refund at this time”) (0145-0146) and, in response to detecting that the target user of the social platform accesses the corresponding transaction link (Examiner interprets that user submits the purchase redemption code; the machine sends it to the central server, which receives and parses the request and extracts the code for lookup. This is the access attempt that triggers validity processing) (“when a user enters and submits a purchase redemption code into a vending machine or automated retail 1501. The machine sends this message to the central server where the message is received and parsed by step 1502. This step extracts the code where it is used to look up the associated information in the data store 1503. Step 1504 determines if the code was found or not and if the code was still valid. If it was not found or not valid, an error message is formed in step 1511 before step 1512 sends the message to the machine that made the request”) (0146),
determining whether the transaction link is within a validity period (Examiner interprets that Mockus establishes a set time for the purchase credential. After that time, the purchase code expires. Upon submission, the server expressly determines whether the code is still valid) (“method of retrieving products from a vending machine or automated retail store by entering in a code that is verified on a central server before dispensing the products. Purchases made through a mobile device are linked to a specific machine to which purchased merchandise can be received. This information is stored in a data store on the central server (FIG. 13) and then the information is synchronized with the machines in the field. This places a temporary hold on the merchandise so it cannot be sold to another consumer. After the set time, the purchase code expires, the hold on the merchandise is lifted and the merchandise becomes available again. The user may apply for a credit or refund at this time … then a user enters and submits a purchase redemption code into a vending machine or automated retail 1501. The machine sends this message to the central server where the message is received and parsed by step 1502. This step extracts the code where it is used to look up the associated information in the data store 1503. Step 1504 determines if the code was found or not and if the code was still valid. If it was not found or not valid, an error message is formed in step 1511 before step 1512 sends the message to the machine that made the request) (0145-0146);
in response to determining that the transaction link is within the validity period, redirecting the target user to a corresponding product transaction page according to the transaction link (Examiner interprets that Mockus teaches that, when a valid matching purchase code is found, processing continues through the purchase-fulfillment procedure) (“determines a matching code was found, the process proceeds to step 1505 which reviews the retrieved information from the data store to determine which type of code is being processed. If the code is a purchase code, the process continues to step 1506. Any other code and the process is routed to the correct subroutine 1507. Step 1506 checks the administrative settings to determine if a Credit Card authorization was required. If not, the process continues to step 1510”) (0147);
and in response to determining that the transaction link exceeds the validity period, returning a transaction link invalidation prompt to the target user (Examiner interprets that If the purchase code is not found or is no longer valid, Mockus forms an error message and sends it to the requesting machine. The returned error message is then displayed to the user. This directly corresponds to returning an invalidation prompt in response to an expired transaction credential) (“when a user enters and submits a purchase redemption code into a vending machine or automated retail 1501. The machine sends this message to the central server where the message is received and parsed by step 1502. This step extracts the code where it is used to look up the associated information in the data store 1503. Step 1504 determines if the code was found or not and if the code was still valid. If it was not found or not valid, an error message is formed in step 1511 before step 1512 sends the message to the machine that made the request. … product search program 200 determines that the live streaming event has not ended (decision step 220, “NO” branch), then product search program 200 returns to step 202 to capture metadata associated with the live streaming event. For example, if product search program 200 determines that the product review of the blogger from a social media broadcast has not ended, then product search program 200 returns to step 202 to capture and store structural and descriptive metadata of a product review of a blogger from a social media broadcast in memory of the server … the user was prompted to swipe the card they bought the product with and a hash checksum was created. This checksum is tested against the checksum stored in the data store 1508. Step 1509 tests the result. If the checksum did not match the stored value, an error message is created in step 1511, otherwise, a correct match will lead to a success message being created in step 1510. This message is then routed to the requesting machine in step 1512. Step 1513 resides on the local machine that made the request. It receives the message from the central server and parses it. Step 1514 checks if the returning message was an error message. If it was, an error message is displayed to the user in step 1515. The process then terminates in step 1526. If the returned message from the central server was not an error message, the machine begins the dispensing process in step 1516. Here the purchased items identification numbers associated with the purchase code that were retrieved from the database in step 1503. Step 1517 verifies that the purchased items are all in stock and available to be dispensed. This is a precautionary step as the inventory was already reserved in a previous procedure. If some of the products are missing, an error message is displayed to the user in step 1515 and the process terminates in step 1526. If all of the products are available to be dispensed, step 1518 displays this information to the user for verification. Step 1519 takes user input to continue or cancel. If the user decides to not vend the purchased merchandise or if there was an error in the list of products, they can cancel the operation and proceed to step 1015 where a user message is displayed and the process then terminates in step 1526. If the user wishes to continue with the vending process, it continues in step 1520 and the machine vends the merchandise. Step 1521 displays a confirmation to the user and formats a return message to the central server to update the purchase code status”) (0146-0148).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, establishing a connection with a social platform and monitoring a live broadcast status of the social platform, parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue, and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, as disclosed by Buckley, monitoring the transaction link, and, in response to detecting that the target user of the social platform accesses the corresponding transaction link, determining whether the transaction link is within a validity period; in response to determining that the transaction link is within the validity period, redirecting the target user to a corresponding product transaction page according to the transaction link; and in response to determining that the transaction link exceeds the validity period, returning a transaction link invalidation prompt to the target user, as taught by Mockus in response to apply expiration and validity-checking technique to online transaction link would prevent use of stale purchase resources after the associated product, inventory reservation, price, or transaction state may have changed to provide the user with an explanatory invalidation message rather than directing the user to an obsolete or unavailable transaction resource.
As per claims 5, Buckley specifically doesn’t disclose, monitoring the product transaction page, and, in response to detecting that the target user performs a payment operation, locking a quantity of the target product in an inventory corresponding to the target product in the product transaction page, however Mockus discloses, monitoring the product transaction page, and, in response to detecting that the target user performs a payment operation (Examiner interprets that Mockus receives a payment request from a user, sends payment information to a payment gateway, receives the gateway response, and determines whether payment was successful) (“purchase subroutine 1300 (FIG. 13) begins when a payment request is made by a user on a mobile device 1301. The purchase subroutine resides inside the central server application and can be accessed by the connected website, mobile website, mobile application or any other authorized program with the proper credentials. This subroutine assumes that a purchase request cannot be made if a product is out of stock. Step 1302 receives the request for the product and the machine and determines what type of payment is being supplied. If the user is using a credit code to pay for a purchase, the supplied code is put into a query in step 1303 and the data store for credit and promotional codes 1304 is accessed to retrieve information. Step 1305 tests to determine if the code was valid and successfully retrieved. If it was, the credit is applied to the balance in step 1306. Step 1307 checks to see if the credit successfully covered the cost of the purchase, if not a message is formed in 1308 that a residual amount is owed through another payment method. If 1307 determines the credit application was successful, step 1309 forms a query to update credit code in the data store 1310 and set a retrieval code that the user will enter into the machine to retrieve their product. The process then continues to step 1311 that formats a success message with the retrieval code. Step 1312 sends the return message to the user that initiated the payment request”) (0141-0142) locking a quantity of the target product in an inventory corresponding to the target product in the product transaction page (Examiner interprets that Mockus places a temporary hold on purchased merchandise so that it cannot be sold to another consumer. The hold remains until the purchase code expires or the product is retrieved. This temporary exclusion from sale is reasonably interpreted as locking the corresponding inventory quantity) (“method of retrieving products from a vending machine or automated retail store by entering in a code that is verified on a central server before dispensing the products. Purchases made through a mobile device are linked to a specific machine to which purchased merchandise can be received. This information is stored in a data store on the central server (FIG. 13) and then the information is synchronized with the machines in the field. This places a temporary hold on the merchandise so it cannot be sold to another consumer. After the set time, the purchase code expires, the hold on the merchandise is lifted and the merchandise becomes available again. The user may apply for a credit or refund at this time … when a user enters and submits a purchase redemption code into a vending machine or automated retail 1501. The machine sends this message to the central server where the message is received and parsed by step 1502. This step extracts the code where it is used to look up the associated information in the data store 1503. Step 1504 determines if the code was found or not and if the code was still valid. If it was not found or not valid, an error message is formed in step 1511 before step 1512 sends the message to the machine that made the request”) (0145-0146),
determining whether the transaction link is within a validity period (Examiner interprets that Mockus first processes the user’s payment request and determines whether payment succeeded. For a successful payment, the system creates and stores a purchase retrieval code. The resulting purchase information is stored and synchronized with the retail machine, placing a temporary hold on the purchased merchandise so that it cannot be sold to another consumer. The examiner interprets this sequence as reserving the purchased inventory in response to successful payment) (“method of retrieving products from a vending machine or automated retail store by entering in a code that is verified on a central server before dispensing the products. Purchases made through a mobile device are linked to a specific machine to which purchased merchandise can be received. This information is stored in a data store on the central server (FIG. 13) and then the information is synchronized with the machines in the field. This places a temporary hold on the merchandise so it cannot be sold to another consumer. After the set time, the purchase code expires, the hold on the merchandise is lifted and the merchandise becomes available again. The user may apply for a credit or refund at this time … then a user enters and submits a purchase redemption code into a vending machine or automated retail 1501. The machine sends this message to the central server where the message is received and parsed by step 1502. This step extracts the code where it is used to look up the associated information in the data store 1503. Step 1504 determines if the code was found or not and if the code was still valid. If it was not found or not valid, an error message is formed in step 1511 before step 1512 sends the message to the machine that made the request) (0145-0146, 0141-0142).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, establishing a connection with a social platform and monitoring a live broadcast status of the social platform, parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue, and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, as disclosed by Buckley, monitoring the product transaction page, and, in response to detecting that the target user performs a payment operation, locking a quantity of the target product in an inventory corresponding to the target product in the product transaction page, as taught by Mockus in response to apply inventory-hold operation to the product transaction page upon successful payment in order to prevent allocation of the paid-for product quantity to another transaction, avoid overselling, and ensure fulfillment of the paid order.
As per claims 6, Buckley specifically doesn’t disclose, obtaining the queue message and generating a temporary order for the target product based on the product information of the target product, however Kruse discloses, obtaining the queue message and generating a temporary order for the target product based on the product information of the target product (Examiner interprets that Kruse processes a matching customer comment to generate a purchase transaction and updates the order database. Kruse’s comments may be classified as “in cart” or “checkout in process” before payment is completed. Those pre-completion order records reasonably correspond to temporary orders) (“product search program 200 determines that the live streaming event has not ended (decision step 220, “NO” branch), then product search program 200 returns to step 202 to capture metadata associated with the live streaming event. For example, if product search program 200 determines that the product review of the blogger from a social media broadcast has not ended, then product search program 200 returns to step 202 to capture and store structural and descriptive metadata of a product review of a blogger from a social media broadcast in memory of the server … Response processing system 130 obtains, standardizes, aggregates, and processes the customer responses from all of the video player applications. Responses from different video player applications or other tools may be transformed to a common format for analysis and further processing. Each response may be tagged with data such as the identifier of the person responding, the site or app from which the response was generated, and a timestamp of when the response was generated. Responses may be processed to generate sales transactions, which result in updates to orders, inventory, and backlog data in database 100. They may also be aggregated, filtered, tagged, sorted, categorized, or otherwise processed, and forwarded to a response viewing feature 117 in the sales administration system 110. User 111 may then view the responses from users, potentially in almost real time, to gauge the reaction to the video stream and to adjust the sales process accordingly. The response viewing feature 117 may also be available to the video production staff performing capture 141 so that video content may be adjusted as desired based on incoming responses.”) (0042-0045).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, establishing a connection with a social platform and monitoring a live broadcast status of the social platform, parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue, and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, as disclosed by Buckley, obtaining the queue message and generating a temporary order for the target product based on the product information of the target product, as taught by Kruse in response to create a preliminary or temporary order from the product-matching message before payment completion so that the selected product, quantity, options, and user identity are preserved thus generating or associating a purchase link with that temporary order to allow the identified user to resume and complete the transaction.
Buckley specifically doesn’t disclose, and generating the transaction link according to the temporary order, however Mockus discloses, and generating the transaction link according to the temporary order (“method of retrieving products from a vending machine or automated retail store by entering in a code that is verified on a central server before dispensing the products. Purchases made through a mobile device are linked to a specific machine to which purchased merchandise can be received. This information is stored in a data store on the central server (FIG. 13) and then the information is synchronized with the machines in the field. This places a temporary hold on the merchandise so it cannot be sold to another consumer. After the set time, the purchase code expires, the hold on the merchandise is lifted and the merchandise becomes available again. The user may apply for a credit or refund at this time”) (0145-0146, 0141-0142).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, establishing a connection with a social platform and monitoring a live broadcast status of the social platform, parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue, and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, as disclosed by Buckley, generating the transaction link according to the temporary order, as taught by Mockus in response to apply inventory-hold operation to the product transaction page upon successful payment in order to prevent allocation of the paid-for product quantity to another transaction, avoid overselling, and ensure fulfillment of the paid order.
Claims 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. 20160092771 (“Buckley”) in view of U.S. Pub. 20200128286 (“Anders”) in view of U.S. Pub. 20220182709 (“Kruse”) in view of U.S. Pub. 20220141538 (“Li”) in view of U.S. Pub. 20100274857 (“Garza”) in view of NPL “Cutting Tail Latency in Data Center Networks with Flow Replication” (“Liu”) in view of U.S. Pub. 20110161403 (“Fu”).
As per claims 7, Buckley specifically doesn’t disclose, loading a data cache module and temporarily storing a connection ID between the data processing component and the social platform through the data cache module, however Fu discloses, loading a data cache module and temporarily storing a connection ID between the data processing component and the social platform through the data cache module (Examiner interprets that Fu’s web server includes client-side caching module 107. The module includes processor/control logic 201, connection detector 203, scripting module 205, and data-collection module 207. Processor 201 executes the caching-module functions and interacts with the other modules to identify the connection and generate cacheable session variables and Fu discloses communication between a web server and a client over a reusable or persistent connection. The connection handles multiple request-response transactions and may be an HTTP Keep-Alive or HTTPS connection and stores session variables in a scripting file and caches the file in local cache 113. Fu also provides an example of a cached private_session_id and teaches that an HTTPS session ID is associated with each connection. The cached session information may be removed by the user or after a predetermined period) (“the UE 101 can access information or web content from a website that employs client-side persistence or caching managed by one or more of the servers 103a-103n. For example, the UE 101 sends a request for the web content to at least one of the servers 103a-103n using a browser application (e.g., session client 111). On receipt of the request, the web server 103 generates web content code (e.g., hypertext markup language (HTML) code) that includes a scripting file (e.g., a JavaScript file) for storing web session information and providing client-side persistence. In one embodiment, the server 103 includes a client-side caching module 107 for generating the client-side persistency scripting file. The scripting file provides logic for: (1) creating session variables to store user identification, authentication, or other session related information; (2) storing the session variables in the scripting file; and then (3) caching the scripting file containing the session variables at the UE 101 (e.g., in the local cache 113 of the UE 101). The cached session variables can then be retrieved from the local cache 113 and used to provide client-side persistency on future requests for the same web content and/or session … session client 111 can then use the cached scripting file and session variables to render the requested web content and provide client-side persistency between multiple web sessions. For example, a user of the UE 101 may close the session client 111, re-launch it, and request the same session again. In this case, the UE 101 already has session variables (e.g., within the cached scripting file) in the local cache 113, and therefore the user will not be prompted by the client-side caching module 107 to provide configuration data, unless the user chooses to reload the session or have session data removed from local cache 113 beforehand … generating cacheable session variables as described with respect to FIG. 1. More specifically, the scripting module 205 determines when a client-side persistency script is cached at the UE 101 for a first time (e.g., when there is not previously stored session data or the session data has been cleared). The detection is performed using the connection supporting connection reuse as described with respect to FIG. 1. If determination is that the scripting file is cached for the first time, the scripting module 205 interacts with the data collection module 207 to collect session configuration information from the UE 101 or the user of the UE 101. In one embodiment, the data collection module 207 initiates transmission of a form to the UE 101 for collecting the information. On receiving the session configuration information, the data collection module 207 can store the information as session variables in for in session database 109 for processing. Then, the scripting module 205 can incorporate the session variables in the scripting file and mark the file and variables for caching at the UE 101 when the scripting module 205 detects a second or subsequent request for the scripting file”) (0024-0034).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, establishing a connection with a social platform and monitoring a live broadcast status of the social platform, parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue, and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, as disclosed by Buckley, loading a data cache module and temporarily storing a connection ID between the data processing component and the social platform through the data cache module, as taught by Fu in response to utilize the connection-identifying session ID, socket ID, worker-process ID, or thread ID among the session variables temporarily stored by caching module to preserve session continuity across subsequent requests to retrieve the active connection mapping without repeatedly identifying the underlying socket or processing thread, maintain continuity between related requests, and remove a stale mapping when the cached session expires.
As per claims 8, Buckley specifically doesn’t disclose, and storing the generated transaction link through the data cache module, however Anders discloses, loading the data cache module and storing the generated transaction link through the data cache module, and/or configuring a validity period for the transaction link stored in the data cache module (Examiner notes that the underlined limitation is disclosed by another prior art. Examiner interprets that Anders retrieves the corresponding product listing from a defined online-store inventory. A selectable online-shopping product listing is reasonably interpreted as a transaction link because it electronically directs the user to the corresponding purchasable product) (“product search program 200 retrieves products from a defined set of sources (e.g., determined by preferences of a user). In one embodiment, product search program 200 returns products from a user defined source. For example, a broadcaster defines preferences that product search program 200 returns products from an inventory listing of a website of the broadcaster. In another example, a broadcaster defines a preference that product search program 200 can return products from an inventory listing of any site available over the Internet (e.g., a default preference). In another embodiment, a user defines a set of preferences that prioritizes sources from which product search program 200 returns products. For example, a broadcaster defines preferences that product search program 200 returns products from a listing of a website of the broadcaster before returning products from inventories of all sites available over the Internet”) (0034).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, establishing a connection with a social platform and monitoring a live broadcast status of the social platform, parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue, and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, as disclosed by Buckley, and storing the generated transaction link through the data cache module, as taught by Anders in response to incorporate product-listing technique that would permit those users to access and purchase the identified products directly, while reducing separate product-search and navigation steps.
Buckley specifically doesn’t disclose, loading the data cache module and/or configuring a validity period for the transaction link stored in the data cache module, however Fu discloses, loading the data cache module and/or configuring a validity period for the transaction link stored in the data cache module (Examiner interprets that Fu’s web server includes client-side caching module 107. The module includes processor/control logic 201, connection detector 203, scripting module 205, and data-collection module 207. Processor 201 executes the caching-module functions and interacts with the other modules to identify the connection and generate cacheable session variables and Fu discloses communication between a web server and a client over a reusable or persistent connection. The connection handles multiple request-response transactions and may be an HTTP Keep-Alive or HTTPS connection and stores session variables in a scripting file and caches the file in local cache 113. Fu also provides an example of a cached private_session_id and teaches that an HTTPS session ID is associated with each connection. The cached session information may be removed by the user or after a predetermined period) (“the UE 101 can access information or web content from a website that employs client-side persistence or caching managed by one or more of the servers 103a-103n. For example, the UE 101 sends a request for the web content to at least one of the servers 103a-103n using a browser application (e.g., session client 111). On receipt of the request, the web server 103 generates web content code (e.g., hypertext markup language (HTML) code) that includes a scripting file (e.g., a JavaScript file) for storing web session information and providing client-side persistence. In one embodiment, the server 103 includes a client-side caching module 107 for generating the client-side persistency scripting file. The scripting file provides logic for: (1) creating session variables to store user identification, authentication, or other session related information; (2) storing the session variables in the scripting file; and then (3) caching the scripting file containing the session variables at the UE 101 (e.g., in the local cache 113 of the UE 101). The cached session variables can then be retrieved from the local cache 113 and used to provide client-side persistency on future requests for the same web content and/or session … session client 111 can then use the cached scripting file and session variables to render the requested web content and provide client-side persistency between multiple web sessions. For example, a user of the UE 101 may close the session client 111, re-launch it, and request the same session again. In this case, the UE 101 already has session variables (e.g., within the cached scripting file) in the local cache 113, and therefore the user will not be prompted by the client-side caching module 107 to provide configuration data, unless the user chooses to reload the session or have session data removed from local cache 113 beforehand … generating cacheable session variables as described with respect to FIG. 1. More specifically, the scripting module 205 determines when a client-side persistency script is cached at the UE 101 for a first time (e.g., when there is not previously stored session data or the session data has been cleared). The detection is performed using the connection supporting connection reuse as described with respect to FIG. 1. If determination is that the scripting file is cached for the first time, the scripting module 205 interacts with the data collection module 207 to collect session configuration information from the UE 101 or the user of the UE 101. In one embodiment, the data collection module 207 initiates transmission of a form to the UE 101 for collecting the information. On receiving the session configuration information, the data collection module 207 can store the information as session variables in for in session database 109 for processing. Then, the scripting module 205 can incorporate the session variables in the scripting file and mark the file and variables for caching at the UE 101 when the scripting module 205 detects a second or subsequent request for the scripting file”) (0024-0034, 0036-0042).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention to obtaining at least one target product information of the target store, creating an information matching table comprising at least one keyword, establishing a connection with a social platform and monitoring a live broadcast status of the social platform, parsing the interactive message and matching the parsed interactive message with the information matching table, in response to the interactive message matching keywords in the information matching table, adding the interactive message to a message queue to form a queue message, and in response to the interactive message not matching keywords in the information matching table, skipping adding the interactive message to the message queue, and determining a number of keywords in the queue message, in response to the queue message comprising a single keyword, determining corresponding target product information based on a corresponding relationship of the keyword, and, in response to that the queue message comprising multiple keywords, calculating a relevance between each keyword and the queue message of a corresponding group, selecting a keyword with a highest relevance, determining corresponding target product information based on the corresponding relationship of the keyword, generating a transaction link according to the target product information, as disclosed by Buckley, loading the data cache module and/or configuring a validity period for the transaction link stored in the data cache module, as taught by Fu in response to utilize the connection-identifying session ID, socket ID, worker-process ID, or thread ID among the session variables temporarily stored by caching module to preserve session continuity across subsequent requests to retrieve the active connection mapping without repeatedly identifying the underlying socket or processing thread, maintain continuity between related requests, and remove a stale mapping when the cached session expires.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. The following references have been cited to further show the state of the art.
U.S. Pub. No. 20040205770 (“Zhang”)
Zhang discloses, method for preventing the delivery of duplicate messages in a message system, wherein each message comprises a unique message identifier that identifies itself from adjacent messages, comprises the steps of polling a message store for messages directed to a specified receiver; receiving from the message store at least one message directed to the specified receiver; processing the at least one message; receiving, from a monitor queue, a message identifier for the last message successfully delivered to the specified receiver; and comparing the message identifier received from the monitor queue to the message identifier of the message received from the message store.
U.S. Pub. No. 20140304753 (“Waltermann”).
Waltermann discloses, method deriving text data from an input of audiovisual data; analyzing the text data to form one or more key words; selecting the one or more key words to obtain one or more relevant key words, wherein selecting comprises selection based on text data from user specific data sources; forming the one or more relevant key words into a query for obtaining relevant content; and issuing one or more queries to obtain related content from a network connected device. Other aspects are described and claimed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GAUTAM UBALE whose telephone number is (571)272-9861. The examiner can normally be reached on Mon-Fri. 7:00 AM- 6:30 PM PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached on (571) 272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GAUTAM UBALE/Primary Examiner, Art Unit 3689
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