DETAILED ACTION
Status of Claims
• The following is an office action in response to the communication filed 03/04/2023.
• Claims 1-18 are currently pending and have been examined.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy of Application No. IN 202241011896, filed on 03/04/2022 has been received.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
First, it is determined whether the claims are directed to a statutory category of invention. See MPEP 2106.03(II). In the instant case, claims 1-6 are directed to a process, claims 7-11 are directed to a machine, and claims 12-18 are directed to a manufacture. Therefore, claims 1-18 are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine if the claims are directed to a judicial exception. See MPEP 2106.04. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong 1 of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong 2 of Step 2A). See MPEP 2106.04.
Taking claim 1 as representative, claim 1 recites at least the following limitations that are believed to recite an abstract idea:
(a) obtaining products data from a plurality of disparate product sources, wherein the products data includes a transaction channel, a product name, a product price, a product specification, a product availability, and a product deal;
(b) extracting at least one contextual attribute from the products data based on a composite and contextual matching technique;
(c) dynamically updating the products data by detecting at least one inconsistency in the products data;
(d) obtaining, from a user, a search query for a product;
(e) generating a recommendation of a transaction mode for the product using a custom model, wherein the transaction mode is a combination of the transaction channel and a financial instrument associated with the user, wherein the recommendation is personalized by the custom model based on at least one of (i) a partial information of financial instruments associated with the user and (ii) a plurality of attributes of the user; and
(f) representing the recommendation for optimizing search of the transaction mode for the product
The above limitations recite the concept of providing transaction recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, the providing of recommendations regarding a transaction represents marketing and sales behaviors. This is illustrated in [0002] of the Specification, describing recommendations for the purpose of sales transactions. Claims 7 and 13 recite the same abstract ideas as claim 1 and accordingly fall within the same grouping of abstract ideas. Accordingly, under Prong One of Step 2A of the MPEP, claims 1, 7, and 13 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the MPEP, claims 1, 7, and 13 recites additional elements, such as automatically obtaining; using software robotics process automation; using a natural language processing model; using software robotics defect detection; at least one user device; a custom machine learning model; the user device; a system; a channel optimization server that comprises a processor and a memory; and one or more non-transitory computer-readable storage medium storing the one or more sequence of instructions, which when executed by the one or more processors, causes to perform a method. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Although these additional computer-related elements are recited, claims 1, 7, and 13 merely invoke such additional elements as a tool to perform the abstract idea. Implementing an abstract idea on a generic computer is not indicative of integration into a practical application. Similar to the limitations of Alice, claims 1, 7, and 13 merely recite a commonplace business method (i.e., providing transaction recommendations) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 1, 7, and 13 generally link the use of the abstract idea to a particular technological environment or field of use. The courts have identified various examples of limitations as merely indicating a field of use/technological environment in which to apply the abstract idea, such as specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer (see FairWarning v. Iatric Sys.). Likewise, claims 1, 7, and 13 specifying that the abstract idea of providing transaction recommendations is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the MPEP, when considered both individually and as a whole, the limitations of claims 1, 7, and 13 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 1, 7, and 13 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 1, 7, and 13 are “directed to” an abstract idea (Step 2A: YES).
Next, under Step 2B, the claims are analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract idea. See MPEP 2106.05. The instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least the following reasons.
Returning to independent claims 1, 7, and 13, these claims recite additional elements, such as automatically obtaining; using software robotics process automation; using a natural language processing model; using software robotics defect detection; at least one user device; a custom machine learning model; the user device; a system; a channel optimization server that comprises a processor and a memory; and one or more non-transitory computer-readable storage medium storing the one or more sequence of instructions, which when executed by the one or more processors, causes to perform a method. As discussed above with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Moreover, the limitations of claims 1, 7, and 13 are manual processes, e.g., receiving information, sending information, etc. The courts have indicated that mere automation of manual processes is not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)). Furthermore, as discussed above with respect to Prong Two of Step 2A, claims 1, 7, and 13 merely recite the additional elements in order to further define the field of use of the abstract idea, therein attempting to generally link the use of the abstract idea to a particular technological environment, such as the Internet or computing networks (see Ultramercial, Inc. v. Hulu, LLC. (Fed. Cir. 2014); Bilski v. Kappos (2010); MPEP 2106.05(h)). Similar to FairWarning v. Iatric Sys., claims 1, 7, and 13 specifying that the abstract idea of providing transaction recommendations is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claim to the computer field, i.e., to execution on a generic computer.
Even when considered as an ordered combination, the additional elements do not add anything that is not already present when they are considered individually. In Alice Corp., the Court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘[a]dd nothing…that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Id. (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, viewed as a whole, claims 1, 7, and 13 simply convey the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in claims 1, 7, and 13 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO).
Dependent claims 2-6, 8-12, and 14-18, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. Dependent claims 2-6, 8-12, and 14-18 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Dependent claims 2-6, 8-12, and 14-18 further identify additional elements, such as software data scrapers; a software text extractor; a natural language processing model; robotic anomaly detection; a robotic quality engine; a serverless processor and neural data streamer; a graphical user interface (GUI) of the at least one user device; at least one user device; training the custom machine learning model; and updating the custom machine learning model. Similar to discussion above the with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). As such, under Step 2A, dependent claims 2-6, 8-12, and 14-18 are “directed to” an abstract idea. Similar to the discussion above with respect to claims 1, 7, and 13, dependent claims 2-6, 8-12, and 14-18 analyzed individually and as an ordered combination, invoke such additional elements as a tool to perform the abstract idea and merely indicate a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, and therefore, do not amount to significantly more than the abstract idea itself. See MPEP 2106.05(f)(2). Accordingly, under the Alice/Mayo test, claims 1-18 are ineligible.
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.
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.
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 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 1, 3-5, 7, 9-11, 13, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Postrel (US 20120130789 A1), hereinafter Postrel, in view of Psota et al. (US 20140258032 A1), hereinafter Psota.
In regards to claim 1, Postrel discloses a method for generating a personalized recommendation by optimizing transaction mode for a product search, said method comprising: (Postrel: [abstract], [0019], [0054]):
(a) automatically obtaining products data from a plurality of disparate product sources using software robotics process automation, wherein the products data includes a transaction channel, a product name, a product price, a product availability, and a product deal (Postrel: [0069] and Fig. 8 – “The trading server has the ability to receive offers from reward servers or merchants (steps 806 and 808) which may then be directed in real time to users based on the database profile information provided by the user or other third party (e.g. an issuer, merchant, etc.) (see FIG. 9). At step 900, the reward server contacts the trading server with an offer to redeem points. Similarly, a merchant may contact the trading server with an offer to be distributed to members (step 902). The trading server records the offer in a database (step 906)”; [0085] – “time sensitive product offerings such as food products or concert tickets, airline departures, hotel room rentals and the like can have an associated diminishing or escalating value based on the length or availability of the offer. This invention may be used to provide hotel rooms such that when rooms are available and the date of use approaches, the rental price may decrease”; [0088] – “Parameters associated with the available quantity, duration, exchange rates, etc may be input into the system to be used in the allocation algorithm to restrict the offer”; [0065] – “the trading server software (i.e. on the search engine computer or another computer)”; [0074] – “the trading server contacts the merchant server to return to the user a list of products that match the user's search criteria”; [0040] – “a user may enter the term ‘DVD Player’…search engine will return a list of web links as well known in the art that will redirect the user to a merchant's web site, in particular to a web page that features the desired item (a DVD Player)”);
(b) extracting at least one contextual attribute from the products data (Postrel: [0069] and Fig. 8 – “The trading server has the ability to receive offers from reward servers or merchants (steps 806 and 808) which may then be directed in real time to users based on the database profile information provided by the user or other third party (e.g. an issuer, merchant, etc.) (see FIG. 9). At step 900, the reward server contacts the trading server with an offer to redeem points. Similarly, a merchant may contact the trading server with an offer to be distributed to members (step 902). The trading server records the offer in a database (step 906)”; [0085] – “time sensitive product offerings such as food products or concert tickets, airline departures, hotel room rentals and the like can have an associated diminishing or escalating value based on the length or availability of the offer. This invention may be used to provide hotel rooms such that when rooms are available and the date of use approaches, the rental price may decrease”; [0018] – “search criteria may also include an availability filter that provides for display to the user of links to web sites that can provide a desired product based on date and time based availability criteria”);
(c) dynamically updating the products data (Postrel: [0089] – “a graphic on a web page that shows the availability of an item, such as the number of items left (or about to expire) for a given offer--similar to a running meter. This meter would be updated in real time so that a user would know when the offer will soon be expired due to unavailability of an item”; [0062] –“ the user may enter requests for certain products or rewards, and if the product or reward is not currently available, then the search engine service may store that request and notify the user when such product or reward becomes available”; [0084-0085] – “Rooms may be booked with discounts that vary in accordance with the number of rooms available, which can change in real time as per the changing availability of rooms…Time sensitive product offerings such as food products or concert tickets, airline departures, hotel room rentals and the like can have an associated diminishing or escalating value based on the length or availability of the offer. This invention may be used to provide hotel rooms such that when rooms are available and the date of use approaches, the rental price may decrease”);
(d) obtaining, from at least one user device associated with a user, a search query for a product (Postrel: [0013] – “user executes a product search using a search engine that accepts a user query”; [0037] and Fig. 4 – “a plurality of reward server computers 10, 12, 14, a trading server 20, a merchant computer 30 and a user computer 40 are shown in communication with a network 2”);
(e) generating a recommendation of a transaction mode for the product using a custom model, wherein the transaction mode is a combination of the transaction channel and a financial instrument associated with the user, wherein the recommendation is personalized by the custom model based on at least one of (i) a partial information of financial instruments associated with the user and (ii) a plurality of attributes of the user (Postrel: [0052] – “user is also able to make comparisons to products from merchants that do not provide such value added offers and determine which offer presents the best terms for the user. For example, the user may be able to determine if any merchants are offering discount coupons or instant rebates that may be used to reduce the purchase price, which merchants may be offering reward points for the purchase of the product, and which merchants do not provide and such offers”; [0054] – “the user may compare similar but not identical DVD players from various merchants to make an informed purchase decision. The search engine may also function to present all comparable product offerings and make recommendations as to the best price, the best value, the best product, or any combination of these factors”; [0069] – “The trading server has the ability to receive offers from reward servers or merchants (steps 806 and 808) which may then be directed in real time to users based on the database profile information provided by the user or other third party (e.g. an issuer, merchant, etc.)”; [0065] – “trading server computer 20 ‘obtains’ the reward points balance information from a reward server 10, 12, 14 stored in the user's account 52 by contacting the appropriate reward server via communication flow 110 (step 608) according to the user's requirements”; [0069] – “user's preferences for manufactured goods services, products, travel destinations, hobbies, interests or any other user entered criteria may be stored in the database for subsequent use by the system (steps 804 and 808). The trading server has the ability to receive offers from reward servers or merchants (steps 806 and 808) which may then be directed in real time to users based on the database profile information provided by the user”; [0079] – “user may have a credit card, debit card, or stored value card that is linked to their points account in such a way as to permit them to pay for purchases with a merchant by using the card, wherein the merchant uses the existing credit card payment infrastructure”; [0019] – “links to web sites that provide optimal purchase value to the user for the desired product may be ranked in order by the search engine web site as determined by the amount of value provided to the user for the desired product”; the merchant is interpreted to be the transaction channel and the coupon/reward/discount to be the transaction mode); and
(f) representing the recommendation at the user device for optimizing search of the transaction mode for the product (Postrel: [0016] – “search criteria may for example include a payment component filter that provides for display to the user of links to web sites that will allow payment for the desired product with a payment component”; [0019] – “search criteria may include an optimal value filter that provides for display to the user of links to web sites that provide optimal purchase value to the user for the desired product as determined by the search engine web site”; see also [0037]).
Postrel further discloses an algorithm (Postrel: [0088]). However, Postrel does not explicitly disclose product data including a product specification; extracting using a natural language processing model based on a composite and contextual matching technique; updating by detecting at least one inconsistency in the products data using software robotics defect detection; and that a model is a machine learning model.
However, Psota teaches a similar method of transactions (Psota: [abstract]), including
product data including a product specification (Psota: [0415] – “supplier may provide an item specification or description”; [0077] – “the specific item is a commodity”);
extracting using a natural language processing model based on a composite and contextual matching technique (Psota: [0079] – “processing the non-public shipping records with natural language processing to detect the entity identifier. In the method, selecting a portion of the plurality of public transactional records comprises natural language processing of data in the public transactional records to identify candidate records for associating with the entity identifier”; see also [0470]);
updating by detecting at least one inconsistency in the products data using software robotics defect detection (Psota: [0401] – “transaction analyzer 4040 may verify these transactions so as to detect any statements in a marketplace participant's profile that are inconsistent with details of a transaction that may impact ratings or may require blocking of the buyers and suppliers registered with the marketplace system. In other words, information in the participant's profile is attempted to be validated by evaluating the content of transaction records associated with the participant. The transaction analyzer 4040 may analyze transaction information 4042 that may include orders, invoices, shipping documents, payment authorization, payment execution, customs documents, security documents and the like. For example, transaction analyzer 4040 may facilitate in verification of the transactions using the records such as customs records that may show an actual import transaction in which the buyer imported goods from the supplier, from a bill of lading, from a bank-issued receipt”; [0202] – “] Data may be further analyzed with a monitoring tool that may look for anomalies, such as peaks, and other statistical measures to identify potentially important events that are captured in the transactions. Analyzing data for peaks and the like may help with activating buyers, suppliers, shippers, and other entities for use in the platform”); and
that a model is a machine learning model (Psota: [0215] – “Machine learning and other artificial intelligence techniques may be applied”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the product analysis and machine learning of Psota in the method of Postrel because Postrel already discloses product data and a model and Psota is merely demonstrating what product data and models may be. Additionally, it would have been obvious to have included product data including a product specification; extracting using a natural language processing model based on a composite and contextual matching technique; updating by detecting at least one inconsistency in the products data using software robotics defect detection; and that a model is a machine learning model as taught by Psota because product analysis and machine learning are well-known and the use of it in a recommendation setting would have improved confidence levels in data analysis (Psota: [0209]).
In regards to claim 3, Postrel/Psota teaches the method of claim 1. Postral further discloses
wherein the user is registered by capturing a personally identifiable information associated with the user at a graphical user interface (GUI) of the at least one user device, wherein the personally identifiable information includes a partial information of the financial instruments associated with the user (Postrel: [0079] – “user may have a credit card, debit card, or stored value card that is linked to their points account in such a way as to permit them to pay for purchases with a merchant by using the card, wherein the merchant uses the existing credit card payment infrastructure as if payment were being made/authorized by a bank linked to the credit card or debit card account, but in fact the card may be linked to the user's points account”; [0069] – “user's preferences for manufactured goods services, products, travel destinations, hobbies, interests or any other user entered criteria may be stored in the database for subsequent use by the system (steps 804 and 808). The trading server has the ability to receive offers from reward servers or merchants (steps 806 and 808) which may then be directed in real time to users based on the database profile information”; [0065] and Fig. 8 – “If the user does not yet have an account (step 602), then the user may be enrolled per the flow diagram of FIG. 8 (step 604) as discussed below. The user, from the user computer, makes a request to the trading server computer 20 via communications flow 102 (step 600), requesting redemption through the network 2 for either all or a portion of the pre-accumulated reward points stored for the user in one of the rewarding entities. A user's reward point account 52 is associated with each of the reward servers but is only shown in FIG. 4 connected to the airline server for sake of clarity. Communications are made by the trading server 20 to the user computer 40 via communications data flows 104. The user may interactively select rewards to be redeemed, or the system may determine which rewards are to be redeemed based on a previously defined user profile rule or other third party profile rule (such as an issuer) (step 606). The trading server computer 20 "obtains" the reward points balance information from a reward server 10, 12, 14 stored in the user's account 52 by contacting the appropriate reward server via communication flow 110 (step 608) according to the user's requirements,”; see also [0022]; [0037] and Fig. 4).
In regards to claim 4, Postrel/Psota teaches the method of claim 3. Postral further discloses
wherein the partial information of the financial instruments is automatically obtained from a financial instrument data source upon authentication of the user using at least one user device (Postrel: [0065] and Fig. 8 – “The trading server computer allows users to "log in" to access the functionality provided where the user may interact with applications, forms or controls. For example, the user may view his account information by using a web browser which may automatically select or allow the user to enter the appropriate identification information and then select buttons, links or other selectable objects to navigate to the part of the system desired. In the alternative, navigation may be done automatically by the web site, and thus be transparent to the user (i.e. not directly controlled by the user). If the user does not yet have an account (step 602), then the user may be enrolled per the flow diagram of FIG. 8 (step 604) as discussed below. The user, from the user computer, makes a request to the trading server computer 20 via communications flow 102 (step 600), requesting redemption through the network 2 for either all or a portion of the pre-accumulated reward points stored for the user in one of the rewarding entities. A user's reward point account 52 is associated with each of the reward servers but is only shown in FIG. 4 connected to the airline server for sake of clarity. Communications are made by the trading server 20 to the user computer 40 via communications data flows 104. The user may interactively select rewards to be redeemed, or the system may determine which rewards are to be redeemed based on a previously defined user profile rule or other third party profile rule (such as an issuer) (step 606). The trading server computer 20 "obtains" the reward points balance information from a reward server 10, 12, 14 stored in the user's account 52 by contacting the appropriate reward server via communication flow 110 (step 608) according to the user's requirements, by using the connection parameters as defined in a database 54 on the trading server as shown in FIG. 5. In one embodiment, the trading server retrieves reward point account balance information via communications flow 114 (step 610) from the reward server for the user. In another embodiment, the trading server transfers as part of the communication 110, the requested reward points to be redeemed (step 612). The reward server computer 10 decreases the user's reward point account 52 by the requested number of reward points (step 614)”).
In regards to claim 5, Postrel/Psota teaches the method of claim 1. Postral further discloses
using a historical transaction data and preferences of the user (Postrel: [0043] – “a payment component is previously-acquired value such as a rebate, coupons, discounts, or reward points”; [0068] – “if a user has a preferred air carrier where the user would like to retain mileage in that reward system, the user may specify a priority of use indicating the reward resources that should be exhausted prior to accessing the most desirable rewards. Following the selection of an item to be acquired, the server may contact all of the reward resources according to this profile to selectively redeem each as required to meet the purchase price”; see also [0051]).
Yet Postrel does not explicitly disclose training the custom machine learning model of step (e).
However, Psota teaches a similar method of transactions (Psota: [abstract]), including
training the custom machine learning model of step (e) (Psota: [0229] – “customs transaction data can be mined to automatically build training data for vertical classification…extracted label ‘red cotton pants’ may be recognized as apparel in our training data. If "red cotton pants" is not recognized, then it can be added to the apparel training data. Generally only a small fraction of customs data has HTS codes; therefore training a classifier and applying the trained commodity entries to new records may facilitate classification of the remainder of the transaction record. Because the vertical classifier may be a self-learning facility, each new record processed by the classifier can enhance the vertical classifier ability to classify new records”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Psota with Postrel for the reasons identified above with respect to claim 1.
In regards to claim 7, claim 7 is directed to a system. Claim 7 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a method. The combined method of Postrel/Psota teaches the limitations of claim 1 as noted above. Postrel further discloses a system for generating a personalized recommendation by optimizing transaction mode for a product search, wherein the system comprises: a channel optimization server that comprises a processor and a memory that are configured to perform (Postrel: [0023]; [0071-0073]). Claim 7 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claims 9-11, all the limitations in system claims 9-11 are closely parallel to the limitations of method claims 3-5 analyzed above and rejected on the same bases.
In regards to claim 13, claim 13 is directed to a medium. Claim 13 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a method. The combined method of Postrel/Psota teaches the limitations of claim 1 as noted above. Postrel further discloses one or more non-transitory computer-readable storage medium storing the one or more sequence of instructions, which when executed by the one or more processors, causes to perform a method for generating a personalized recommendation by optimizing transaction mode for a product search comprising (Postrel: [0023]; [0071-0073]). Claim 13 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claims 15-17, all the limitations in medium claims 15-17 are closely parallel to the limitations of method claims 3-5 analyzed above and rejected on the same bases.
Claims 2, 8, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Postrel, in view of Psota, in view of Collard et al. (US 20220207465 A1), hereinafter Collard.
In regards to claim 2, Postrel/Psota teaches the method of claim 1. Postral further discloses
wherein automatically obtaining products data from a plurality of disparate product sources further comprises: automatically obtaining products data from the plurality of disparate product sources using software; interpreting and extracting relevant product data (Postrel: [0069] and Fig. 8 – “The trading server has the ability to receive offers from reward servers or merchants (steps 806 and 808) which may then be directed in real time to users based on the database profile information provided by the user or other third party (e.g. an issuer, merchant, etc.) (see FIG. 9). At step 900, the reward server contacts the trading server with an offer to redeem points. Similarly, a merchant may contact the trading server with an offer to be distributed to members (step 902). The trading server records the offer in a database (step 906)”; [0085] – “time sensitive product offerings such as food products or concert tickets, airline departures, hotel room rentals and the like can have an associated diminishing or escalating value based on the length or availability of the offer. This invention may be used to provide hotel rooms such that when rooms are available and the date of use approaches, the rental price may decrease”; [0088] – “Parameters associated with the available quantity, duration, exchange rates, etc may be input into the system to be used in the allocation algorithm to restrict the offer”; [0065] – “the trading server software (i.e. on the search engine computer or another computer)”; [0074] – “the trading server contacts the merchant server to return to the user a list of products that match the user's search criteria”; [0040] – “a user may enter the term ‘DVD Player’…search engine will return a list of web links as well known in the art that will redirect the user to a merchant's web site, in particular to a web page that features the desired item (a DVD Player)”);
extracting at least one contextual attribute from the products data (Postrel: [0069] and Fig. 8 – “The trading server has the ability to receive offers from reward servers or merchants (steps 806 and 808) which may then be directed in real time to users based on the database profile information provided by the user or other third party (e.g. an issuer, merchant, etc.) (see FIG. 9). At step 900, the reward server contacts the trading server with an offer to redeem points. Similarly, a merchant may contact the trading server with an offer to be distributed to members (step 902). The trading server records the offer in a database (step 906)”; [0085] – “time sensitive product offerings such as food products or concert tickets, airline departures, hotel room rentals and the like can have an associated diminishing or escalating value based on the length or availability of the offer. This invention may be used to provide hotel rooms such that when rooms are available and the date of use approaches, the rental price may decrease”; [0018] – “search criteria may also include an availability filter that provides for display to the user of links to web sites that can provide a desired product based on date and time based availability criteria”);
the extracted product data (Postrel: [0069] and Fig. 8 – “The trading server has the ability to receive offers from reward servers or merchants (steps 806 and 808) which may then be directed in real time to users based on the database profile information provided by the user or other third party (e.g. an issuer, merchant, etc.) (see FIG. 9). At step 900, the reward server contacts the trading server with an offer to redeem points. Similarly, a merchant may contact the trading server with an offer to be distributed to members (step 902). The trading server records the offer in a database (step 906)”); and
using a robotic quality engine for data structure quality control to ensure the accuracy and completeness of the extracted data (Postrel: [0089] – “a graphic on a web page that shows the availability of an item, such as the number of items left (or about to expire) for a given offer--similar to a running meter. This meter would be updated in real time so that a user would know when the offer will soon be expired due to unavailability of an item”; [0062] –“ the user may enter requests for certain products or rewards, and if the product or reward is not currently available, then the search engine service may store that request and notify the user when such product or reward becomes available”; [0084-0085] – “Rooms may be booked with discounts that vary in accordance with the number of rooms available, which can change in real time as per the changing availability of rooms…Time sensitive product offerings such as food products or concert tickets, airline departures, hotel room rentals and the like can have an associated diminishing or escalating value based on the length or availability of the offer. This invention may be used to provide hotel rooms such that when rooms are available and the date of use approaches, the rental price may decrease”).
Yet Postrel does not explicitly disclose product data including using software data scrapers; using a software text extractor; using a natural language processing model based on a composite and contextual matching technique; using robotic anomaly detection to detect and correct inconsistencies in data; and using a serverless processor and neural data streamer for data parsing and cleanup.
However, Psota teaches a similar method of transactions (Psota: [abstract]), including
using software data scrapers (Psota: [0010] – “a process of scraping a plurality of sources of data for information that is relevant to the marketplace participant”; [0232] – “one or more technologies such as a data scraping technology to collect or retrieve data from one or more sources to analyze the data and determine ratings for the suppliers. The data scraping technology may implement one or more applications that may be configured to operate on resources, such as data stores, to identify data and/or data types. Once identified, the data and/or data types may be reorganized, reclassified, manipulated, further stored, modified, and/or changed according to a selected data model/paradigm. For example, the data collection step 902 may implement data scraping related technology to retrieve data from the web about companies that may supply goods and/or services to the buyers”);
using a software text extractor; using a natural language processing model based on a composite and contextual matching technique (Psota: [0079] – “processing the non-public shipping records with natural language processing to detect the entity identifier. In the method, selecting a portion of the plurality of public transactional records comprises natural language processing of data in the public transactional records to identify candidate records for associating with the entity identifier”; see also [0470]);
using robotic anomaly detection to detect and correct inconsistencies in data (Psota: [0401] – “transaction analyzer 4040 may verify these transactions so as to detect any statements in a marketplace participant's profile that are inconsistent with details of a transaction that may impact ratings or may require blocking of the buyers and suppliers registered with the marketplace system. In other words, information in the participant's profile is attempted to be validated by evaluating the content of transaction records associated with the participant. The transaction analyzer 4040 may analyze transaction information 4042 that may include orders, invoices, shipping documents, payment authorization, payment execution, customs documents, security documents and the like. For example, transaction analyzer 4040 may facilitate in verification of the transactions using the records such as customs records that may show an actual import transaction in which the buyer imported goods from the supplier, from a bill of lading, from a bank-issued receipt”; [0202] – “] Data may be further analyzed with a monitoring tool that may look for anomalies, such as peaks, and other statistical measures to identify potentially important events that are captured in the transactions. Analyzing data for peaks and the like may help with activating buyers, suppliers, shippers, and other entities for use in the platform”);
for data parsing and cleanup (Psota: [0010] – “a process of scraping a plurality of sources of data for information that is relevant to the marketplace participant”; [0232] – “one or more technologies such as a data scraping technology to collect or retrieve data from one or more sources to analyze the data and determine ratings for the suppliers. The data scraping technology may implement one or more applications that may be configured to operate on resources, such as data stores, to identify data and/or data types. Once identified, the data and/or data types may be reorganized, reclassified, manipulated, further stored, modified, and/or changed according to a selected data model/paradigm. For example, the data collection step 902 may implement data scraping related technology to retrieve data from the web about companies that may supply goods and/or services to the buyers”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Psota with Postrel for the reasons identified above with respect to claim 1.
Additionally, Collard teaches a similar method of transactions (Collard: [0007]), including
using a serverless processor and neural data streamer (Collard: [0045] – “a rules and regulations database 32 may be stored on the one or more administrator computer systems, which may at least in part reside in the cloud 34. Some subset 36 of the set of administrator computer systems may provide the computing power for computationally intensive Artificial Intelligence programs 38, particularly neural networks which may require continual training.”; [0055] – “pre-processed data is then entered into a first stream of a predictive neural network 407, with a second stream comprising corresponding floorplan data 408 and a third stream comprising categorization or characterization data, such as the number of bathrooms, the number of common areas, etc.”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the cloud and neural streamer of Collard in the method of Postrel/Psota because Postrel/Psota already discloses computing elements and a model and Collard is merely demonstrating what computing elements and models may be. Additionally, it would have been obvious to have included using a serverless processor and neural data streamer as taught by Collard because clouds and neural streams are well-known and the use of it in a recommendation setting would have improved predictions (Collard: [abstract]).
The examiner notes that “to ensure the accuracy and completeness of the extracted data” and “for data parsing and cleanup,” in this claim are merely intended use/result and are accordingly granted little to no patentable weight. Nevertheless, the limitations have been fully examined.
In regards to claim 8, all the limitations in system claim 8 are closely parallel to the limitations of method claim 2 analyzed above and rejected on the same bases.
In regards to claim 14, all the limitations in medium claim 14 are closely parallel to the limitations of method claim 2 analyzed above and rejected on the same bases.
Claims 6, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Postrel, in view of Psota, in view of Kurian et al. (US 20190171975 A1), hereinafter Kurian.
In regards to claim 6, Postrel/Psota teaches the method of claim 1.
Yet Postrel does not explicitly disclose product data including wherein the custom machine learning model of step (e) is updated in real-time based on a response of the user to the recommendation.
However, Kurian teaches a similar method of transactions (Kurian: [0043]), including
wherein the custom machine learning model of step (e) is updated in real-time based on a response of the user to the recommendation (Kurian: [0074] – “the arrangements described provide for use of machine learning datasets to identify characteristics of an identified object, compare the characteristics to a pre-defined goal or limit, generate recommendations, and the like. As discussed herein, these functions may be performed in real-time or near real-time to enable efficient, informed decisioning. The system also enables real-time or near real-time feedback to be provided to a system in order to update and/or validate one or more machine learning datasets based on whether a recommendation was implemented, whether an item was purchased, or the like”; [0031] – “store data associated with various users, event processing device parameter information, account information, historical transaction or other user data, user behavioral information associated with a device, and the like. In some examples, this data may include purchase data that may be used to update and/or validate one or more machine learning datasets”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the updating of Kurian in the method of Postrel/Psota because Postrel/Psota already discloses a model and Kurian is merely demonstrating that a model may be updated. Additionally, it would have been obvious to have included wherein the custom machine learning model of step (e) is updated in real-time based on a response of the user to the recommendation as taught by Kurian because updating is well-known and the use of it in a recommendation setting would have provided efficient recommendations (Kurian: [0004]).
In regards to claim 12, all the limitations in system claim 12 are closely parallel to the limitations of method claim 6 analyzed above and rejected on the same bases.
In regards to claim 18, all the limitations in medium claim 18 are closely parallel to the limitations of method claim 6 analyzed above and rejected on the same bases.
Conclusion
The prior art made of record and not relied upon is considered pertinent to