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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action.
In response to Examiner's communication of 3/24/2025, Applicant responded on 6/24/2025. Amended claims 2, 8, 9, 11, and 17.
Claims 2-21 are pending in this application and have been examined.
Response to Amendment
Applicant's amendments to claims 2, 8, 9, 11, and 17 are not sufficient to overcome the 101 rejections set forth in the previous action.
Applicant's amendments to claims 2, 8, 9, 11, and 17 are sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…that at least the above highlighted additional elements recited in the claims integrate the abstract idea into a practical application of configuring, training, and utilizing multiple neural networks to work together to decompose payment transactions in order to provide a partner fee recommendation to a marketplace provider. As discussed in the Application, "classification of payment transactions and other factors is a challenging task for neural networks" (Application, paragraph [0017]). The claimed solution enables "a neural network to be trained to more accurately classify and associate aspects of payment transactions" (Id)….These additional elements "impose[s] a meaningful limit on the judicial exception," such that amended claim 2 as a whole is more than a drafting effort designed to monopolize the exception, as clearly there are numerous other ways to use the alleged abstract idea (which Applicant does not concede) beyond what is recited in amended claim 2. Based on the 2019 PEG, these additional elements should not be evaluated as to whether they are "well-understood, routine, conventional activity," as "a claim that includes conventional elements may still integrate an exception into a practical application, thereby satisfying the subject matter eligibility requirement of Section 101." Furthermore, Applicant respectfully submits that amended claim 2 can be closely analogized to the claim in Example 39 of the Subject Matter Eligibility Examples, which was published in conjunction with the 2019 PEG. The claim in Example 39 is directed to a method for training a neural network for facial detection…Amended claim 2 herein is directed to utilizing a neural network system that includes multiple neural networks and trained using words extracted from previously conducted transactions to classify subsequent transactions (or components from each subsequent transaction). Similar to the claim in Example 39, claim 2 as amended herein does not recite any mathematical relationships, formulas, or calculations. Furthermore, similar to the claim in Example 39, claim 2 as amended herein is directed to configuring, training, and utilizing a computer-based model, which "are not practically performed in the human mind." Accordingly, Applicant respectfully asserts that the pending claims are not directed to an abstract idea as the claims integrate the abstract idea into a practical application under Step 2A, Prong Two of the Alice/Mayo Test...” The Examiner respectfully disagrees.
While Applicant’s amendments further prosecution, unlike Example 39, which recite specific neutral network training steps and specific facial animation steps that are not practically performed in the human mind. The amended neural network elements in the present application are recited at a high level of generality and do not recite specific neural network training steps for the two recited neural networks in specificity, since the recited neural networks are pre-trained respectively, and thus are being interpreted as additional computing components recited at a high level of generality applying the abstract ideas.
As such, under the broadest reasonable interpretation, the claims are directed to mental process (i.e. human analyzing payment transactions, human extracting words and payment information, human calculate and suggesting fees based on analyzing payment transactions) and certain methods of organizing human activities (i.e. human assessing business partner fee calculations for business partners, merchants and users, which is commercial interactions and managing personal behavior or relationships or interactions between people), under Step 2A Prong1, generally linked to a technical environment and using generic computing components and two pre-trained neural networks receiving or transmitting data and performing extra solution activities to apply the identified abstract ideas, thus do not integrate the identified abstract ideas into a practical application under Step 2A Prong2 or amount to significantly more under Step 2B.
Examiner invites Applicant to schedule an interview with the Examiner at Applicant’s convenience to expedite the prosecution of the present application.
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered. Examiner find persuasive Applicant’s remarks on pg10-11.
The closest prior art are US20170091722A1 to Miyamoto et al., (hereinafter referred to as “Miyamoto”) in view of US Patent Publication to US10949825B1 to Brosamer et al., (hereinafter referred to as “Brosamer”) in view of US Patent Publication to US20070288312A1 to Wang, (hereinafter referred to as “Wang”)
However, the teachings of the references do not teach the specific ordered sequence of limitations of independent claims 2, 11, 17.
None of the references teaches the following claim features required by claims 2, 11:
accessing, by a computer system, payment transaction data associated with a first plurality of payment transactions that has been processed by a payment provider for a plurality of marketplace providers between a merchant and a plurality of users;
extracting, by the computer system and from the payment transaction data and for each payment transaction from the first plurality of payment transactions, words that describe the payment transaction;
decomposing, by the computer system and using a first neural network, each payment transaction in the first plurality of payment transactions into a corresponding partner fee portion and a corresponding purchase price portion based on the extracted words and one or more parameters associated with the payment transaction, wherein the first neural network was trained to decompose payment transactions using root words that are tagged with different portions of a second plurality of payment transactions;
linking, by the computer system, the corresponding partner fee portion and the corresponding purchase price portion of each payment transaction in the first plurality of transactions;
providing, by the computer system, the linked corresponding partner fee portion and the linked corresponding purchase price portion of each payment transaction as related input values to a second neural network, wherein the second neural network is trained to determine partner fee recommendations based on linked portions of historic payment transactions;
determining, based on an output of the second neural network, a partner fee recommendation for a marketplace provider of the plurality of marketplace providers for transactions associated with the merchant and subsequently conducted via the marketplace provider; and
providing the partner fee recommendation to the marketplace provider.
None of the references teaches the following claim features required by claims 17:
accessing payment transaction data corresponding to a first plurality of payment transactions that has been processed by a payment provider for a plurality of marketplace providers between a merchant account and a plurality of user accounts;
extracting, from the payment transaction data and for each payment transaction from the first plurality of payment transactions, words that describe the payment transaction;
decomposing, using a first neural network, each respective payment transaction from the first plurality of payment transactions into a corresponding purchase price portion or a corresponding partner fee portion based on the extracted words, wherein the first neural network was trained to decompose payment transactions using root words extracted from a second plurality of payment transactions;
linking the corresponding partner fee portion and the corresponding purchase price portion of each respective payment transaction in the first plurality of transactions;
providing the corresponding partner fee portion and the corresponding purchase price portion of each respective payment transaction as input values to a second neural network based on the linking, wherein the second neural network is trained to determine partner fee recommendations based on linked portions of historic payment transactions;
determining, based on an output of the second neural network, a partner fee recommendation for a marketplace provider of the plurality of marketplace providers for subsequent transactions conducted through the merchant account via the marketplace provider; and
providing the partner fee recommendation to the marketplace provider.
Furthermore, Examiner presents Non-Patent Literature, “Data Mining in Electronic Commerce” to Banks et al, 9/7/2006, hereinafter Banks discloses, — Modern business is rushing toward e-commerce. If the transition is done properly, it enables better management, new services, lower transaction costs and better customer relations. Success depends on skilled information technologists, among whom are statisticians. This paper focuses on some of the contributions that statisticians are making to help change the business world, especially through the development and application of data mining methods. This is a very large area, and the topics we cover are chosen to avoid overlap with other papers in this special issue, as well as to respect the limitations of our expertise. Inevitably, electronic commerce has raised and is raising fresh research problems in a very wide range of statistical areas, and we try to emphasize those challenges. However, Banks does not teach the specific ordered sequence of limitations of independent claims 2, 11, 17, nor otherwise cure the deficiencies of Miyamoto, Brosamer, Wang.
Moreover, since the specific ordered combined sequence of claim elements recited in claims 2, 11, 17, cannot be found in the cited prior art and can only be found as recited in Applicant’s Specification, any combination of the cited references and/or additional references(s) to teach all the claim elements, including the features discussed above, would be the result of impermissible hindsight reconstruction. Accordingly, any combination with Miyamoto, Brosamer, Wang, Banks, and/or any other additional reference(s) would be improper to teach the claimed invention.
The prior art rejection is hereby withdrawn.
Claim Objection
Claim 17 is objected due to the following informalities.
Claim 17 recites,
“…into a corresponding purchase price portion or a corresponding partner fee portion based on the extracted words…”,
“…linking the corresponding partner fee portion and the corresponding purchase price portion…”,
“…providing the corresponding partner fee portion and the corresponding purchase price portion…”.
It is not clear if Claim 17 is meant to recite “…into a corresponding purchase price portion and a corresponding partner fee portion based on the extracted words…”, since the later limitations in Claim 17 recite “…the corresponding partner fee portion and the corresponding purchase price portion…”.
The limitation in Claim 17 is being interpretated as typographical error, and being interpretated as “…a corresponding purchase price portion and a corresponding partner fee portion…”, in accordance with the other limitations in Claim 17 and mirroring Claim 2 and 11. Appropriate correction required.
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 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 2 (similarly 11, 17) recites, “A method, comprising:
accessing, by a …, payment transaction data associated with a first plurality of payment transactions that has been processed by a payment provider for a plurality of marketplace providers between a merchant and a plurality of users;
extracting, by the … and from the payment transaction data and for each payment transaction from the first plurality of payment transactions, words that describe the payment transaction;
decomposing, by the … and using a first …, each payment transaction in the first plurality of payment transactions into a corresponding partner fee portion and a corresponding purchase price portion based on the extracted words and one or more parameters associated with the payment transaction, wherein the first … was trained to decompose payment transactions using root words that are tagged with different portions of a second plurality of payment transactions;
linking, by the …, the corresponding partner fee portion and the corresponding purchase price portion of each payment transaction in the first plurality of transactions;
providing, by the …, the linked corresponding partner fee portion and the linked corresponding purchase price portion of each payment transaction as related input values to a second …, wherein the second … is trained to determine partner fee recommendations based on linked portions of historic payment transactions;
determining, based on an output of the second …, a partner fee recommendation for a marketplace provider of the plurality of marketplace providers for transactions associated with the merchant and subsequently conducted via the marketplace provider; and
providing the partner fee recommendation to the marketplace provider.
Analyzing under Step 2A, Prong 1:
The limitations regarding, …accessing, by a …, payment transaction data associated with a first plurality of payment transactions that has been processed by a payment provider for a plurality of marketplace providers between a merchant and a plurality of users; extracting, by the … and from the payment transaction data and for each payment transaction from the first plurality of payment transactions, words that describe the payment transaction; decomposing, by the … and using a first …, each payment transaction in the first plurality of payment transactions into a corresponding partner fee portion and a corresponding purchase price portion based on the extracted words and one or more parameters associated with the payment transaction, wherein the first … was trained to decompose payment transactions using root words that are tagged with different portions of a second plurality of payment transactions; linking, by the …, the corresponding partner fee portion and the corresponding purchase price portion of each payment transaction in the first plurality of transactions; providing, by the …, the linked corresponding partner fee portion and the linked corresponding purchase price portion of each payment transaction as related input values to a second …, wherein the second … is trained to determine partner fee recommendations based on linked portions of historic payment transactions; determining, based on an output of the second …, a partner fee recommendation for a marketplace provider of the plurality of marketplace providers for transactions associated with the merchant and subsequently conducted via the marketplace provider; and providing the partner fee recommendation to the marketplace provider.…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to, …accessing, by a …, payment transaction data associated with a first plurality of payment transactions that has been processed by a payment provider for a plurality of marketplace providers between a merchant and a plurality of users; extracting, by the … and from the payment transaction data and for each payment transaction from the first plurality of payment transactions, words that describe the payment transaction; decomposing, by the … and using a first …, each payment transaction in the first plurality of payment transactions into a corresponding partner fee portion and a corresponding purchase price portion based on the extracted words and one or more parameters associated with the payment transaction, wherein the first … was trained to decompose payment transactions using root words that are tagged with different portions of a second plurality of payment transactions; linking, by the …, the corresponding partner fee portion and the corresponding purchase price portion of each payment transaction in the first plurality of transactions; providing, by the …, the linked corresponding partner fee portion and the linked corresponding purchase price portion of each payment transaction as related input values to a second …, wherein the second … is trained to determine partner fee recommendations based on linked portions of historic payment transactions; determining, based on an output of the second …, a partner fee recommendation for a marketplace provider of the plurality of marketplace providers for transactions associated with the merchant and subsequently conducted via the marketplace provider; and providing the partner fee recommendation to the marketplace provider…; therefore, the claims are directed to a mental process.
Further, the limitations regarding, …accessing, by a …, payment transaction data associated with a first plurality of payment transactions that has been processed by a payment provider for a plurality of marketplace providers between a merchant and a plurality of users; extracting, by the … and from the payment transaction data and for each payment transaction from the first plurality of payment transactions, words that describe the payment transaction; decomposing, by the … and using a first …, each payment transaction in the first plurality of payment transactions into a corresponding partner fee portion and a corresponding purchase price portion based on the extracted words and one or more parameters associated with the payment transaction, wherein the first … was trained to decompose payment transactions using root words that are tagged with different portions of a second plurality of payment transactions; linking, by the …, the corresponding partner fee portion and the corresponding purchase price portion of each payment transaction in the first plurality of transactions; providing, by the …, the linked corresponding partner fee portion and the linked corresponding purchase price portion of each payment transaction as related input values to a second …, wherein the second … is trained to determine partner fee recommendations based on linked portions of historic payment transactions; determining, based on an output of the second …, a partner fee recommendation for a marketplace provider of the plurality of marketplace providers for transactions associated with the merchant and subsequently conducted via the marketplace provider; and providing the partner fee recommendation to the marketplace provider…, under the broadest reasonable interpretation, human assessing business partner fee calculations for business partners, merchants and users, which is commercial interactions and managing personal behavior or relationships or interactions between people, therefore, the claims are directed to organizing human activities.
Accordingly, the claims are directed to a mental process, organizing human activities, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 2, 11, 17: computer system, neural network, A device, comprising: a non-transitory memory storing instructions; and a hardware processor configured to execute the instructions to cause the device, A non-transitory machine-readable medium
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “accessing …”, “…input…”, “…output…”, “providing…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “accessing …”, “…input…”, data output –“…output…”, “providing…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0019] Fig. 1 is a schematic diagram showing a system according to an embodiment of the disclosure. In certain embodiments, the system shown in Fig. 1 may include or implement one or more electronic devices such as mobile devices, desktop computers, servers, and/or software components that operate to perform various transactions or processes. It can be appreciated that the system illustrated in Fig. 1 may be deployed in other ways and that the operations performed and/or the services provided by the electronic devices described herein may be combined or separated for a given implementation and may be performed by a greater number or fewer number of devices.
[0020] The system of Fig. 1 may include a server device 102 and a client device 104. The devices of the system may communicate with one or more other devices over a network 160. Server device 102 may be maintained by or associated with a payment provider, a merchant, or another service provider. Server device 102 may be configured to receive data associated with payment transactions, store the data, determine aspects of the payment transactions such as the partner fee amount of the payment transactions, receive other data, and determine a partner fee recommendation. In certain embodiments, server device 102 may be integrated into one or more servers. Such servers may additionally be configured to perform other operations, such as payment or transaction processing. Thus, server device 102 may be maintained by a service provider, such as PayPal®, Inc. of San Jose, CA. Such servers may also include one or more databases storing data (e.g., historical transactions or device data) related to the determined partner fee recommendations or previously received or classified data. "Transaction," as used herein, refers to any suitable action where an item, content/data, or service is exchanged for consideration, such as payment.
[0021] Server device 102 and client device 104 may each include one or more processors, memories, and other appropriate components for executing instructions such as program code and/or data stored on one or more computer readable mediums to implement the various applications, data, and steps described herein. For example, such instructions may be stored in one or more computer readable media such as memories or data storage devices internal and/or external to the devices and/or accessible over network 160. Network 160 may be implemented as a single network or a combination of multiple networks. For example, in various embodiments, network 160 may include the Internet and/or one or more intranets, landline networks, wireless networks, and/or other appropriate types of networks. Server device 102 and client device 104 may be implemented using any appropriate hardware and software configured for wired and/or wireless communication over network 160.
[0022] In certain embodiments, server device 102 may include an input device 125 that may include components (e.g., a touch screen, a mouse, a keyboard, and other input device) configured to receive user inputs, provide outputs to the user, and/or otherwise allow for operation of server device 102.
[0023] Software 135 may include applications to perform functions and processes described herein or provide other features for server device 102. For example, software 135 may include security applications for implementing client-side security features, programmatic client applications for interfacing with appropriate application programming interfaces (APIs) over network 160, or other types of applications. Software 135 may also include applications that enable transfer of information, processing of payments, and otherwise allow conducting of transactions through the service provider as described herein.
[0024] In certain embodiments, various APIs may allow for operation of software 135, operation of one or more machine learning networks or neural devices, communication with other devices (e.g., client device 104), storage, lookup, and management of data, calculation of values, analysis of payment transactions (e.g., to identify a transaction fee), and/or other operations. Such APIs may be implemented as one application or may be a combination of APIs from multiple applications. As such, APIs may be used to allow for programs that perform the techniques described herein.
[0025] Server device 102 may include hardware 120, which may be similar to hardware described herein. Hardware 120 may be configured to allow operation (e.g., by providing processing resources, cooling, and/or other performing other operations) of server device 102.
[0026] Server device 102 may also include a database 145 that stores data associated with payment transactions, recommendations, partner fees, and other such data. Such data may include historical data, current data, data from transactions performed by the service provider, data obtained from other sources, and/or other such data. Database 145 may also include data identifying one or more associated parties. Such associated parties may, for example, be a merchant or a platform provider.
[0027] Client device 104 may be a device operated by a merchant, a marketplace platform provider, a customer, and/or another party associated with sales or transactions provided through a marketplace platform. Client device 104 may include any number of hardware, software, input devices, and databases as described herein for server device 102, as well as other components.
[0028] In various embodiments, the system of Fig. 1 or portions thereof may be used to provide a partner fee recommendation. Fig. 2A is a block diagram showing a system for determining a partner fee recommendation according to an embodiment of the disclosure. Fig. 2A illustrates a system that includes communications circuitry 202, a transaction analyzer 206, a database 208, a fee curator 210, a partner fee recommender module 212, and a marketplace partner 214. Database 208 may be similar to database 145 of Fig. 1. Communications circuitry 202 may be a portion of hardware 120 of Fig. 1. Transaction analyzer 206, fee curator 210, and partner recommender module 212 may be implemented as software 135 of Fig. 1.
[0029] Communications circuitry 202 may receive data from one or more external sources. Such sources may be, for example, one or more marketplace platforms, merchants, or buyers. For example, communications circuitry 202 may receive data from buyer device 204A, merchant device 204B, and/or service provider device 204C. Such data may include payment transaction data, sales data, conducted transaction data, offerings data, and onboarding data. Such data are further described in Fig. 2B. Thus, in addition to communicating with communications circuitry 202, buyer device 204A, merchant device 204B, and/or service provider device 204C may also communicate amongst each other to conduct the transaction. In certain embodiments, communications circuitry 202 may directly communicate with buyer device 204A, merchant device 204B, and/or service provider device 204C. Thus, the partner fee recommender may receive data from each member of a transaction independently (e.g., receive data from all three of buyer device 204A, merchant device 204B, and service provider device 204C who are all involved in a single transaction). Furthermore, by receiving data directly from each of buyer device 204A, merchant device 204B, and service provider device 204C, the partner fee recommender may receive data conducted by the merchant over multiple marketplace platforms (e.g., marketplace platforms in addition to that of service provider device 204C). Thus, the partner fee recommender may receive data conducted by merchants and/or buyers over a plurality of different marketplace platforms.
[0030] In various embodiments, data received from communications circuitry 202 may be stored and/or used to determine a partner fee recommendation (e.g., a recommended fee structure for the marketplace partner such as a recommended flat fee or percentage-based fee). Thus, communications circuitry 202 may provide the received data to transaction analyzer 206. Transaction analyzer 206 may first analyze (e.g., break down into component parts such as breaking down a payment transaction into multiple components that factor into the cost of the payment transaction) and/or transform the data and then provide the data to database 208. Database 208 may then store the data for use in curating and determining a partner fee recommendation. In certain other embodiments, data received from communications circuitry 202 may first be stored within a database, or may be stored after processing by various other elements of the system (e.g., after processing by fee curator 210).
[0031] Database 208 may provide data to fee curator 210. Such data may include, for example, payment transaction data directed to one or more payment transactions conducted or listed on the marketplace platform. Fee curator 210 may be configured to classify a payment transaction as a purchase transaction (e.g., payment for goods or services), a partner fee transaction (e.g., payment for utilization of the marketplace platform), or another such transaction. Fee curator 210 may, additionally or alternatively, be configured to break down the payment transaction into component parts that include a purchase transaction portion and/or a partner fee transaction portion.
[00101] Where applicable, various embodiments provided by the present disclosure may be implemented using hardware, software, or combinations of hardware and software. Also, where applicable, the various hardware components and/or software components set forth herein may be combined into composite components comprising software, hardware, and/or both without departing from the spirit of the present disclosure. Where applicable, the various hardware components and/or software components set forth herein may be separated into sub-components comprising software, hardware, or both without departing from the scope of the present disclosure. In addition, where applicable, it is contemplated that software components may be implemented as hardware components and vice-versa.
[00102] Software in accordance with the present disclosure, such as program code and/or data, may be stored on one or more computer readable mediums. It is also contemplated that software identified herein may be implemented using one or more general purpose or specific purpose computers and/or computer systems, networked and/or otherwise. Where applicable, the ordering of various steps described herein may be changed, combined into composite steps, and/or separated into sub-steps to provide features described herein.
[00103] The various features and steps described herein may be implemented as systems
comprising one or more memories storing various information described herein and one or more processors coupled to the one or more memories and a network, wherein the one or more processors are operable to perform steps as described herein, as non-transitory machine-readable medium comprising a plurality of machine-readable instructions which, when executed by one or more processors, are adapted to cause the one or more processors to perform a method comprising steps described herein, and methods performed by one or more devices, such as a hardware processor, user device, server, and other devices described herein.
[00104] The foregoing description is provided to enable a person skilled in the art to practice the various configurations described herein. While the subject technology has been particularly described with reference to the various figures and configurations, it should be understood that these are for illustration purposes only and should not be taken as limiting the scope of the subject technology.
[00105] There may be many other ways to implement the subject technology. Various functions and elements described herein may be partitioned differently from those shown without departing from the scope of the subject technology. Various modifications to these configurations will be readily apparent to those skilled in the art, and generic principles defined herein may be applied to other configurations. Thus, many changes and modifications may be made to the subject technology, by one having ordinary skill in the art, without departing from the scope of the subject technology.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 2-21 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/PO HAN LEE/Examiner, Art Unit 3623