DETAILED ACTION
This action is in reply to the Amendments filed on 07/21/2026.
Claims 6-7, 9, and 15-16 are canceled.
Claims 1-5, 8, 10-14 and 17-20 are rejected.
Claims 1-5, 8, 10-14 and 17-20 are currently pending and have been examined.
Response to Amendment
Applicant’s amendment, filed 07/21/2026, has been entered. Claims 1-3, 8, 10-12, and 17-18 have been amended.
Claim Objections
The claim objections from the prior Office Action have been withdrawn pursuant Applicant’s amendments.
Priority
This patent Application claims priority from Foreign Application No. CN202111079045.9 filed 09/15/2021. This benefit has been received and acknowledged and therefore, the instant claims receive the effective filing date of 09/15/2021.
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 1-5, 8, 10-14, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories (see MPEP 2106.03). All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the Subject Matter Eligibility Test, it is determined whether the claims are directed to a judicially recognized exception (see MPEP 2106.04). Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 8 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-one or more processors; and
-a storage apparatus, storing one or more programs thereon,
-the one or more programs, when executed by the one or more processors, cause the one or more processors to implement operations, the operations comprising:
-acquiring content association information between orders in a set of to-be-processed orders, wherein the content association information is used for representing whether different orders comprise a same service content;
-respectively determining, based on the content association information, backlog influence degrees of the orders in the set of to-be-processed orders, wherein a backlog influence degree of an order represents an influence degree of the order on a backlog state of the orders in the set of to-be-processed orders; and
-determining, based on the backlog influence degrees, a processing sequence of the orders in the set of to-be-processed orders, and performing order processing according to the determined processing sequence,
-wherein the determining, based on the backlog influence degrees, the processing sequence of the orders in the set of to-be-processed orders, and performing order processing according to the determined processing sequence, comprises:
-determining a target attribute of a preceding order set of each order in the set of to-be- processed orders, wherein the target attribute is used for indicating whether the preceding order set is empty, wherein each order in the set of to-be-processed orders includes a same service content as orders in the preceding order set, respectively, and each order is generated no earlier than the orders in its preceding order set;
-determining, based on the target attribute of the preceding order set of each order and the backlog influence degree of each order, a processing sequence of each order, wherein an order having an empty preceding order set is processed earlier than an order having a non-empty preceding order set, and an order having a high backlog influence degree is processed earlier than an order having a low backlog influence degree; and
-selecting an order having an empty preceding order set from the set of to-be-processed orders and adding the selected order to an order processing queue, and performing processing steps as follows:
-in response to determining that the order processing queue is non-empty and there is a current idle thread, selecting an order having the highest backlog influence degree from the order processing queue as a candidate order, processing the candidate order using the idle thread, and deleting the candidate order from the order processing queue;
-releasing the occupied thread in response to determining that the processing of the candidate order is completed, and deleting an association relationship between the candidate order and orders in a succeeding order set of the candidate order; and
-updating, in response to determining that the set of to-be-processed orders comprises an unprocessed order, the order processing queue to continue to perform the processing steps
The above limitations recite the concept of determining a processing sequence of received orders. The above limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a).
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations of acquiring content association information between orders in a set of to-be-processed orders, wherein the content association information is used for representing whether different orders comprise a same service content; respectively determining, based on the content association information, backlog influence degrees of the orders in the set of to-be-processed orders, wherein a backlog influence degree of an order represents an influence degree of the order on a backlog state of the orders in the set of to-be-processed orders; and determining, based on the backlog influence degrees, a processing sequence of the orders in the set of to-be-processed orders, and performing order processing according to the determined processing sequence, wherein the determining, based on the backlog influence degrees, the processing sequence of the orders in the set of to-be-processed orders, and performing order processing according to the determined processing sequence, comprises: determining a target attribute of a preceding order set of each order in the set of to-be- processed orders, wherein the target attribute is used for indicating whether the preceding order set is empty, wherein each order in the set of to-be-processed orders includes a same service content as orders in the preceding order set, respectively, and each order is generated no earlier than the orders in its preceding order set; determining, based on the target attribute of the preceding order set of each order and the backlog influence degree of each order, a processing sequence of each order, wherein an order having an empty preceding order set is processed earlier than an order having a non-empty preceding order set, and an order having a high backlog influence degree is processed earlier than an order having a low backlog influence degree; and selecting an order having an empty preceding order set from the set of to-be-processed orders and adding the selected order to an order processing queue, and performing processing steps as follows: in response to determining that the order processing queue is non-empty and there is a current idle thread, selecting an order having the highest backlog influence degree from the order processing queue as a candidate order, processing the candidate order using the idle thread, and deleting the candidate order from the order processing queue; releasing the occupied thread in response to determining that the processing of the candidate order is completed, and deleting an association relationship between the candidate order and orders in a succeeding order set of the candidate order; and updating, in response to determining that the set of to-be-processed orders comprises an unprocessed order, the order processing queue to continue to perform the processing steps are processes that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “acquiring,” “determining,” “determining,” “determining,” “determining,” “determining,” “selecting,” “selecting,” “releasing,” and “updating” in the context of this claim encompass advertising, and marketing or sales activities.
Similarly, the limitation of the one or more programs, when executed by the one or more processors, cause the one or more processors to implement operations is a process that, under its broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the operations are implemented by the one or more processors executed by one or more programs, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “the one or more programs, when executed by the one or more processors, cause the one or more processors to” language, “implement” in the context of this claim encompasses advertising, and marketing or sales activities.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
-one or more processors; and
-a storage apparatus, storing one or more programs thereon,
-the one or more programs, when executed by the one or more processors, cause the one or more processors to implement operations, the operations comprising:
-acquiring content association information between orders in a set of to-be-processed orders, wherein the content association information is used for representing whether different orders comprise a same service content;
-respectively determining, based on the content association information, backlog influence degrees of the orders in the set of to-be-processed orders, wherein a backlog influence degree of an order represents an influence degree of the order on a backlog state of the orders in the set of to-be-processed orders; and
-determining, based on the backlog influence degrees, a processing sequence of the orders in the set of to-be-processed orders, and performing order processing according to the determined processing sequence,
-wherein the determining, based on the backlog influence degrees, the processing sequence of the orders in the set of to-be-processed orders, and performing order processing according to the determined processing sequence, comprises:
-determining a target attribute of a preceding order set of each order in the set of to-be- processed orders, wherein the target attribute is used for indicating whether the preceding order set is empty, wherein each order in the set of to-be-processed orders includes a same service content as orders in the preceding order set, respectively, and each order is generated no earlier than the orders in its preceding order set;
-determining, based on the target attribute of the preceding order set of each order and the backlog influence degree of each order, a processing sequence of each order, wherein an order having an empty preceding order set is processed earlier than an order having a non-empty preceding order set, and an order having a high backlog influence degree is processed earlier than an order having a low backlog influence degree; and
-selecting an order having an empty preceding order set from the set of to-be-processed orders and adding the selected order to an order processing queue, and performing processing steps as follows:
-in response to determining that the order processing queue is non-empty and there is a current idle thread, selecting an order having the highest backlog influence degree from the order processing queue as a candidate order, processing the candidate order using the idle thread, and deleting the candidate order from the order processing queue;
-releasing the occupied thread in response to determining that the processing of the candidate order is completed, and deleting an association relationship between the candidate order and orders in a succeeding order set of the candidate order; and
-updating, in response to determining that the set of to-be-processed orders comprises an unprocessed order, the order processing queue to continue to perform the processing steps
These limitations are not indicative of integration into a practical application because:
The additional elements of claim 8 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) as supported by paragraph [0027] of Applicant’s specification – “It should be noted that the method for processing an order provided by embodiments of the present disclosure is generally performed by the server 105, and accordingly, the apparatus for processing an order is generally provided in the server 105.” Specifically, the additional elements of an apparatus, one or more processors, a storage apparatus, and storing one or more programs thereon are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of acquiring data [i.e. receiving data], determining data, selecting data, releasing data, and updating data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of claim 8, taken individually or as a whole, the additional elements of claim 8 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Claim 1 is a method reciting similar functions as claim 8. Claim 1 does not recite any additional elements, accordingly, claim 1 does not qualify as eligible subject matter for similar reasons as claim 8 indicated above.
Claim 10 is a non-transitory computer readable storage medium reciting similar functions as claim 8. Examiner notes that claim 10 recites the additional elements of a non-transitory computer readable storage medium, a computer program, and a processor, however, claim 10 does not qualify as eligible subject matter for similar reasons as claim 8 indicated above.
Therefore, claims 1, 8, and 10 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 2-5, 11-14, and 17-20, 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. More specifically, dependent claims 2-5, 11-14, and 17-20 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 2-5, 11-14, and 17-20 do not recite any farther additional elements, and as such are not indicative of integration into a practical application for at least similar reasons discussed above. As such, under prong two of Step 2A, claims 2-5, 11-14, and 17-20 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 2-7 and 11-20 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 8, and 10, dependent claims 2-5, 11-14, and 17-20 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. determining a processing sequence of received orders) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 8, 10-14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bonig et al. (US 2018/0167492 A1), hereinafter Bonig, in view of Baptist et al. (US 2019/0102252 A1), hereinafter Baptist.
Regarding claim 1, Bonig discloses a method for processing orders, the method comprising:
-acquiring content association information between orders in a set of to-be-processed orders, wherein the content association information is used for representing whether different orders comprise a same service content (Bonig, see at least: “it may be determined whether the incoming transaction comprises an order to buy or sell a quantity of the associated financial instrument or an order to modify or cancel an existing order in the electronic market [i.e. acquiring content association information between orders in a set of to-be-processed orders]” [0115] and “the message management module 140 may determine the transaction type of the transaction requested in a given message. A message may include an instruction to perform a type of transaction [i.e. wherein the content association information is used for representing whether different orders comprise a same service content]. The transaction type may be, in one embodiment, a request/offer/order to either buy or sell a specified quantity or units of a financial instrument at a specified price or value” [0096] and “It should also be appreciated that the multiple components and systems discussed herein may be synchronized or coordinated to process all of the same transactions/instructions (e.g., be redundant). The systems and components discussed herein may be configured to be immediately consistent (e.g., process all of the same transactions/instructions at the same or substantially same actual or real time) or to be eventually consistent (e.g., process all of the same transactions/instructions at different actual or real time) [i.e. wherein the content association information is used for representing whether different orders comprise a same service content]” [0278]);
-respectively determining, based on the content association information, backlog influence degrees of the orders in the set of to-be-processed orders, wherein a backlog influence degree of an order represents an influence degree of the order on a backlog state of the orders in the set of to-be-processed orders (Bonig, see at least: “specific characteristics of market activity taken by market participants may provide an indication of a particular market participant's effect on market liquidity. For example, a Market Quality Index (“MQI”) of an order may be determined using the characteristics. An MQI may be considered a value indicating a likelihood that a particular order will improve or facilitate liquidity in a market. That is, the value may indicate a likelihood that the order will increase a probability that subsequent requests and transaction from other market participants will be satisfied [i.e. respectively determining backlog influence degrees of the orders in the set of to-be-processed orders, wherein a backlog influence degree of an order represents an influence degree of the order on a backlog state of the orders in the set of to-be- processed orders]. As such, an MQI may be determined based on … a size of the entered order, a volume or quantity of previously filled orders of the market participant associated with the order, and/or a frequency of modifications to previous orders of the market participant associated with the order. In this way, an electronic trading system may function to assess and/or assign an MQI to received electronic messages to establish messages that have a higher value to the system, and thus the system may use computing resources more efficiently by expending resources to match orders of the higher value messages prior to expending resources of lower value messages” [0125] and “The order processing module may also store data indicative of quantities and associated prices of orders to buy or sell a product placed in the market order book 110, as associated with particular market participants [i.e. based on the content association information]” [0120]); and
-determining, based on the backlog influence degrees, a processing sequence of the orders in the set of to-be-processed orders, and performing order processing according to the determined processing sequence (Bonig, see at least: “specific characteristics of market activity taken by market participants may provide an indication of a particular market participant's effect on market liquidity. For example, a Market Quality Index (“MQI”) of an order may be determined using the characteristics. An MQI may be considered a value indicating a likelihood that a particular order will improve or facilitate liquidity in a market. That is, the value may indicate a likelihood that the order will increase a probability that subsequent requests and transaction from other market participants will be satisfied [i.e. based on the backlog influence degrees]. As such, an MQI may be determined based on … a size of the entered order, a volume or quantity of previously filled orders of the market participant associated with the order, and/or a frequency of modifications to previous orders of the market participant associated with the order. In this way, an electronic trading system may function to assess and/or assign an MQI to received electronic messages to establish messages that have a higher value to the system, and thus the system may use computing resources more efficiently by expending resources to match orders of the higher value messages prior to expending resources of lower value messages [i.e. determining a processing sequence of the orders in the set of to-be-processed orders, and performing order processing according to the determined processing sequence]” [0125] and “Orders having a higher priority may be matched before orders of a lower priority. This priority may be determined using various techniques. For example, orders that were indicated by messages received earlier may receive a higher priority to match than orders that were indicated by messages received later … scoring or grading of the characteristics may provide for priority determination. Data indicative of order matches may be stored by a match engine and/or an order processing module 136, and used for determining MQI scores of market participants” [0123] and “the order processing function 136 receives incoming transactions from the market participants 404 and ensures deterministic processing thereof, i.e. that the incoming transactions are processed according to the defined business rules of the electronic trading system 100 [i.e. performing order processing according to the determined processing sequence] … The order processing function 136 may then further generate, or cause to be generated, appropriate acknowledgements and/or market data based thereon which are then communicated to the market participants 404” [0250]),
-wherein the determining, based on the backlog influence degrees, the processing sequence of the orders in the set of to-be-processed orders, and performing order processing according to the determined processing sequence (Bonig, see at least: “Orders having a higher priority may be matched before orders of a lower priority. This priority may be determined using various techniques. For example, orders that were indicated by messages received earlier may receive a higher priority to match than orders that were indicated by messages received later … scoring or grading of the characteristics may provide for priority determination. Data indicative of order matches may be stored by a match engine and/or an order processing module 136, and used for determining MQI scores of market participants [i.e. the determining, based on the backlog influence degrees, the processing sequence of the orders in the set of to-be-processed orders, and performing order processing according to the determined processing sequence comprises:]” [0123] and “the order processing function 136 receives incoming transactions from the market participants 404 and ensures deterministic processing thereof, i.e. that the incoming transactions are processed according to the defined business rules of the electronic trading system 100 [i.e. performing order processing according to the determined processing sequence] … The order processing function 136 may then further generate, or cause to be generated, appropriate acknowledgements and/or market data based thereon which are then communicated to the market participants 404” [0250]), comprises:
-determining a target attribute of a preceding order set of each order in the set of to-be- processed orders, wherein the target attribute is used for indicating whether the preceding order set is empty, wherein each order in the set of to-be-processed orders includes a same service content as orders in the preceding order set, respectively, and each order is generated no earlier than the orders in the preceding order set (Bonig, see at least: “the action associated with the transaction is determined. For example, it may be determined whether the incoming transaction comprises an order to buy or sell a quantity of the associated financial instrument [i.e. determining a target attribute of a preceding order set of each order in the set of to-be- processed orders] or an order to modify or cancel an existing order in the electronic market” [0115] and “the exchange may further define the matching algorithm, or rules, by which incoming orders will be matched/allocated to resting orders” [0127] and “Order Level Priority Pro Rata, also referred to as Threshold Pro Rata, is similar to the Price (or ‘Vanilla’) Pro Rata algorithm but has a volume threshold defined … The Threshold Pro Rata sequence of events is: … 1. Extract all potential matching orders at best price from the order book into a list … Sort the list by explicit time priority, oldest timestamp first [i.e. wherein the target attribute is used for indicating whether the preceding order set is empty and each order is generated no earlier than the orders in the preceding order set]. This is the matching list … Find the ‘Matching volume’, which is the total volume of all the orders in the matching list [i.e. wherein each order in the set of to-be-processed orders includes a same service content as orders in the preceding order set, respectively] … Find the ‘tradable volume’, which is the smallest of the matching volume and the volume left to trade on the incoming order … Allocate volume to each order in the matching list in turn, starting at the beginning of the list” [0168-0173] Examiner notes that the oldest timestamp has an empty proceeding order set and that the orders being matched in a particular market of the exchange are all for the same product type [i.e. includes a same service]);
-determining, based on the target attribute of the preceding order set of each order and the backlog influence degree of each order, a processing sequence of each order, wherein an order having an empty preceding order set is processed earlier than an order having a non-empty preceding order set, and an order having a high backlog influence degree is processed earlier than an order having a low backlog influence degree (Bonig, see at least: “a first-in/first-out (FIFO) matching algorithm, also referred to as a “Price Time” algorithm, considers each identified order sequentially in accordance with when the identified order was received … Some exchange computer systems provide a priority to certain standing orders in particular markets. An example of such an order is the first order that improves a price (i.e., improves the market) for the product during a trading session. To be given priority, the trading platform may require that the quantity associated with the order is at least a minimum quantity [i.e. determining, based on the target attribute of the preceding order set of each order and the backlog influence degree of each order, a processing sequence of each order]” [0138] and “specific characteristics of market activity taken by market participants may provide an indication of a particular market participant's effect on market liquidity. For example, a Market Quality Index (“MQI”) of an order may be determined using the characteristics. An MQI may be considered a value indicating a likelihood that a particular order will improve or facilitate liquidity in a market. That is, the value may indicate a likelihood that the order will increase a probability that subsequent requests and transaction from other market participants will be satisfied [i.e. based on the target attribute of the preceding order set of each order and the backlog influence degree of each order]. As such, an MQI may be determined based on … a size of the entered order, a volume or quantity of previously filled orders of the market participant associated with the order, and/or a frequency of modifications to previous orders of the market participant associated with the order. In this way, an electronic trading system may function to assess and/or assign an MQI to received electronic messages to establish messages that have a higher value to the system, and thus the system may use computing resources more efficiently by expending resources to match orders of the higher value messages prior to expending resources of lower value messages [i.e. an order having a high backlog influence degree is processed earlier than an order having a low backlog influence degree]” [0125] and “Order Level Priority Pro Rata, also referred to as Threshold Pro Rata, is similar to the Price (or ‘Vanilla’) Pro Rata algorithm but has a volume threshold defined. Any pro rata allocation below the threshold will be rounded down to 0. The initial pass of volume allocation is carried out in using pro rata; the second pass of volume allocation is carried out using Price Explicit Time. The Threshold Pro Rata sequence of events is: … 1. Extract all potential matching orders at best price from the order book [i.e. for each order in the set of to-be-processed orders] into a list … Sort the list by explicit time priority, oldest timestamp first [i.e. wherein an order having an empty preceding order set is processed earlier than an order having a non-empty preceding order set]. This is the matching list … Find the ‘Matching volume’, which is the total volume of all the orders in the matching list … Find the ‘tradable volume’, which is the smallest of the matching volume and the volume left to trade on the incoming order … Allocate volume to each order in the matching list in turn, starting at the beginning of the list” [0168-0173]); and
-selecting an order having an empty preceding order set from the set of to-be-processed orders and adding the selected order to an order processing queue (Bonig, see at least: “Order Level Priority Pro Rata, also referred to as Threshold Pro Rata, is similar to the Price (or ‘Vanilla’) Pro Rata algorithm but has a volume threshold defined … The Threshold Pro Rata sequence of events is: … 1. Extract all potential matching orders at best price from the order book into a list … Sort the list by explicit time priority, oldest timestamp first [i.e. selecting an order having an empty preceding order set from the set of to-be-processed orders]. This is the matching list … Find the ‘Matching volume’, which is the total volume of all the orders in the matching list … Find the ‘tradable volume’, which is the smallest of the matching volume and the volume left to trade on the incoming order … Allocate volume to each order in the matching list in turn, starting at the beginning of the list” [0168-0173] and “Transaction receiver 510 receives and sequences, or orders, all of the messages it receives from the multiple sources, including client computers 502 and 504. Transaction receiver 510 may also add time signal data to each received message. The transaction receiver 510 then sends the ordered messages to the multiple transaction processors 508 [i.e. and adding the selected order to an order processing queue]. The data path for sequenced messages sent from transaction receiver 510 to the transaction processors 508 is path 524” [0302] and “It should be appreciated that the combination of the transaction receiver 510 and a bus architecture (where all the match engines pull data from the orderer off a bus architecture) ensures that the transaction processors 508 (e.g., match engines) [i.e. and adding the selected order to an order processing queue] receive messages (e.g., financial transactions, instruction identifiers, etc.) in the same order” [0309] Examiner notes that the oldest timestamp has an empty proceeding order set), and performing processing steps as follows:
-in response to determining that the order processing queue is non-empty and there is a current idle thread, selecting an order having the highest backlog influence degree from the order processing queue as a candidate order, processing the candidate order using the idle thread (Bonig, see at least: “The exchange computer system monitors incoming orders received thereby and attempts to identify, i.e., match or allocate, as described herein, one or more previously received, but not yet matched, orders, i.e., limit orders to buy or sell a given quantity at a given price, referred to as “resting” orders [i.e. in response to determining that the order processing queue is non-empty and there is a current idle thread], stored in an order book database, wherein each identified order is contra to the incoming order and has a favorable price relative to the incoming order” [0129] and “transaction processors may be hardware matching processors that match or attempt to match incoming messages with messages counter thereto, as described above. Transaction processor 508 transmits electronic data transaction result messages [i.e. processing the candidate order using the idle thread] to client computer 502 via output path 528. Transaction processor 509 transmits electronic data transaction result messages to client computer 504 via output path 546” [0336] and “specific characteristics of market activity taken by market participants may provide an indication of a particular market participant's effect on market liquidity. For example, a Market Quality Index (“MQI”) of an order may be determined using the characteristics. An MQI may be considered a value indicating a likelihood that a particular order will improve or facilitate liquidity in a market. That is, the value may indicate a likelihood that the order will increase a probability that subsequent requests and transaction from other market participants will be satisfied … In this way, an electronic trading system may function to assess and/or assign an MQI to received electronic messages to establish messages that have a higher value to the system, and thus the system may use computing resources more efficiently by expending resources to match orders of the higher value messages prior to expending resources of lower value messages [i.e. selecting an order having the highest backlog influence degree from the order processing queue as a candidate order, processing the candidate order using the idle thread]” [0125]).
Bonig does not explicitly disclose deleting the candidate order from the order processing queue; releasing the occupied thread in response to determining that the processing of the candidate order is completed, and deleting an association relationship between the candidate order and orders in a succeeding order set of the candidate order; and updating, in response to determining that the set of to-be-processed orders comprises an unprocessed order, the order processing queue to continue to perform the processing steps.
Baptist, however, teaches prioritizing requests (i.e. abstract), including the known technique of deleting the candidate order from the order processing queue (Baptist, see at least: “before the first time t1 94, the computing device receives requests 1-5 from requesters operating in the DSN. At the first-time period t1 94, none of the requests have yet been processed, thus the queue 95 includes requests 1-5. As illustrated, the requests are listed in order of the prioritization score. For example, request 1 has a prioritization score of 91 and will be executed first, request 3 has a prioritization score of 85 and will be executed second, request 2 has a prioritization score of 77 and will be executed third, request 4 has a prioritization score of 65 and will be executed fourth and request 5 has a prioritization score of 58 and will be executed fifth. During time period t1 94, the computing device 88 processes request 1, deletes request 1 from the queue [i.e. deleting the candidate order from the order processing queue], and receives request 6 and request 7 and obtains respective prioritization scores (e.g., 83 for request 6 and 62 for request 7)” [0046]);
the known technique of releasing the occupied thread in response to determining that the processing of the candidate order is completed, and deleting an association relationship between the candidate order and orders in a succeeding order set of the candidate order (Baptist, see at least: “before the first time t1 94, the computing device receives requests 1-5 from requesters operating in the DSN. At the first-time period t1 94, none of the requests have yet been processed, thus the queue 95 includes requests 1-5. As illustrated, the requests are listed in order of the prioritization score. For example, request 1 has a prioritization score of 91 and will be executed first, request 3 has a prioritization score of 85 and will be executed second, request 2 has a prioritization score of 77 and will be executed third, request 4 has a prioritization score of 65 and will be executed fourth and request 5 has a prioritization score of 58 and will be executed fifth. During time period t1 94, the computing device 88 processes request 1, deletes request 1 from the queue [i.e. deleting an association relationship between the candidate order and the orders in the succeeding order set of the candidate order], and receives request 6 and request 7 and obtains respective prioritization scores (e.g., 83 for request 6 and 62 for request 7)” [0046] and “At time period t2 96, the queue 95 now includes, in order of prioritization score, request 2, request 6, request 3, request 4, request 7, and request 5. Note the computing device may re-obtain the prioritization score for one or more requests (e.g., request 2 score increasing from 77 in time t1 to 84 in time t2). Also note the computing device may re-obtain the prioritization score for a request based on one of when determining a prioritization score for a new pending request, after a timeframe, after a certain number of new requests, and a command [i.e. releasing the occupied thread in response to determining that the processing of the candidate order is completed]” [0047]); and
the known technique of updating, in response to determining that the set of to-be-processed orders comprises an unprocessed order, the order processing queue to continue to perform the processing steps (Baptist, see at least: “At time period t2 96, the queue 95 now includes, in order of prioritization score, request 2, request 6, request 3, request 4, request 7, and request 5. Note the computing device may re-obtain the prioritization score for one or more requests (e.g., request 2 score increasing from 77 in time t1 to 84 in time t2). Also note the computing device may re-obtain the prioritization score for a request [i.e. updating the order processing queue to continue to perform the processing steps] based on one of when determining a prioritization score for a new pending request [i.e. in response to determining that the set of to-be-processed orders comprises an unprocessed order], after a timeframe, after a certain number of new requests, and a command” [0047]). These known techniques are applicable to the method of Bonig as they both share characteristics and capabilities, namely, they are directed to prioritizing requests.
It would have been recognized that applying the known techniques of deleting the candidate order from the order processing queue; releasing the occupied thread in response to determining that the processing of the candidate order is completed, and deleting an association relationship between the candidate order and orders in a succeeding order set of the candidate order; and updating, in response to determining that the set of to-be-processed orders comprises an unprocessed order, the order processing queue to continue to perform the processing steps, as taught by Baptist, to the teachings of Bonig would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modifications of deleting the candidate order from the order processing queue; releasing the occupied thread in response to determining that the processing of the candidate order is completed, and deleting an association relationship between the candidate order and orders in a succeeding order set of the candidate order; and updating, in response to determining that the set of to-be-processed orders comprises an unprocessed order, the order processing queue to continue to perform the processing steps, as taught by Baptist, into the method of Bonig would have been recognized by those of ordinary skill in the art as resulting in an improved method that would improve the response time for completion of the service (Baptist, [0005]).
Regarding claim 2, Bonig in view of Baptist teaches the method of claim 1. Bonig further discloses:
-wherein the backlog influence degree of the order in the set of to-be-processed orders is used for characterizing a number of associated orders of the order in the set of to-be-processed orders, wherein service contents in an associated order of the order and service contents in a non-associated order of the order in the set of to-be-processed orders have no intersection (Bonig, see at least: “specific characteristics of market activity taken by market participants may provide an indication of a particular market participant's effect on market liquidity. For example, a Market Quality Index (“MQI”) of an order may be determined using the characteristics. An MQI may be considered a value indicating a likelihood that a particular order will improve or facilitate liquidity in a market. That is, the value may indicate a likelihood that the order will increase a probability that subsequent requests and transaction from other market participants will be satisfied [i.e. wherein the backlog influence degree of the order in the set of to-be-processed orders is used for characterizing a number of associated orders of the order in the set of to-be-processed orders]. As such, an MQI may be determined based on … a size of the entered order, a volume or quantity of previously filled orders of the market participant associated with the order, and/or a frequency of modifications to previous orders of the market participant associated with the order. In this way, an electronic trading system may function to assess and/or assign an MQI to received electronic messages to establish messages that have a higher value to the system, and thus the system may use computing resources more efficiently by expending resources to match orders of the higher value messages prior to expending resources of lower value messages” [0125] and “An exchange provides one or more markets for the purchase and sale of various types of products including financial instruments such as stocks, bonds, futures contracts, options, currency, cash, and other similar instruments. Agricultural products and commodities are also examples of products traded on such exchanges … each exchange establishes a specification for each market provided thereby that defines at least the product traded [i.e. wherein the order, orders in the preceding order set, and orders in the succeeding order set have the same service content, respectively] in the market” [0127] Examiner notes that the products in the orders being matched in a particular market of the exchange are different than the types of products in the orders being matched in a different market of the exchange [i.e. wherein service contents in an associated order of the order and service contents in a non-associated order of the order in the set of to-be-processed orders have no intersection]).
Regarding claim 3, Bonig in view of Baptist teaches the method of claim 2. Bonig further discloses:
-wherein the method further comprises:
-for each order in the set of to-be-processed orders, determining, based on the content association information, the preceding order set and a succeeding order set of the order, wherein the order, orders in the preceding order set, and orders in the succeeding order set have the same service content, respectively, and the order is generated no later than the orders in the succeeding order set (Bonig, see at least: “Order Level Priority Pro Rata, also referred to as Threshold Pro Rata, is similar to the Price (or ‘Vanilla’) Pro Rata algorithm but has a volume threshold defined. Any pro rata allocation below the threshold will be rounded down to 0. The initial pass of volume allocation is carried out in using pro rata; the second pass of volume allocation is carried out using Price Explicit Time. The Threshold Pro Rata sequence of events is: … 1. Extract all potential matching orders [i.e. based on the content association information] at best price from the order book [i.e. for each order in the set of to-be-processed orders] into a list … Sort the list by explicit time priority, oldest timestamp first [i.e. determining, based on the content association information, the preceding order set and a succeeding order set of the order and the order is generated no later than the orders in the succeeding order set]. This is the matching list … Find the ‘Matching volume’, which is the total volume of all the orders in the matching list [i.e. wherein the order, orders in the preceding order set, and orders in the succeeding order set have the same service content] … Find the ‘tradable volume’, which is the smallest of the matching volume and the volume left to trade on the incoming order … Allocate volume to each order in the matching list in turn, starting at the beginning of the list” [0168-0173] and “An exchange provides one or more markets for the purchase and sale of various types of products including financial instruments such as stocks, bonds, futures contracts, options, currency, cash, and other similar instruments. Agricultural products and commodities are also examples of products traded on such exchanges … each exchange establishes a specification for each market provided thereby that defines at least the product traded [i.e. wherein the order, orders in the preceding order set, and orders in the succeeding order set have the same service content, respectively] in the market” [0127] Examiner notes that the orders being matched in a particular market of the exchange are all for the same product type [i.e. have the same service content]).
Regarding claim 4, Bonig in view of Baptist teaches the method of claim 3. Bonig further discloses:
-wherein the determining, based on the content association information, the preceding order set and the succeeding order set of the order (Bonig, see at least: “Order Level Priority Pro Rata, also referred to as Threshold Pro Rata, is similar to the Price (or ‘Vanilla’) Pro Rata algorithm but has a volume threshold defined. Any pro rata allocation below the threshold will be rounded down to 0. The initial pass of volume allocation is carried out in using pro rata; the second pass of volume allocation is carried out using Price Explicit Time. The Threshold Pro Rata sequence of events is: … 1. Extract all potential matching orders [i.e. based on the content association information] at best price from the order book into a list … Sort the list by explicit time priority, oldest timestamp first [i.e. determining, based on the content association information, the preceding order set and the succeeding order set of the order]. This is the matching list” [0168-0173], comprises:
-determining, from orders that are generated no later than the order in the set of to-be-processed orders, a target order corresponding to each service content in the order to obtain a target order set, wherein a target order corresponding to a service content is an order comprising the service content and has the latest generation time (Bonig, see at least: “the market may operate using characteristics that involve collecting orders over a period of time, such as a batch auction market. In such an embodiment, the period of time may be considered an order accumulation period. The period of time may involve a beginning time and an ending time, with orders placed in the market after the beginning time, and the placed order matched at or after the ending time [i.e. from orders that are generated no later than the order in the set of to-be-processed orders and has the latest generation time]. As such, the action associated with an order extracted from a message may involve placing the order in the market within the period of time [i.e. determining a target order corresponding to each service content in the order to obtain a target order set]. Also, electronic messages may be received prior to or after the beginning time of the period of time” [0102] and “Order Level Priority Pro Rata, also referred to as Threshold Pro Rata, is similar to the Price (or ‘Vanilla’) Pro Rata algorithm but has a volume threshold defined … The Threshold Pro Rata sequence of events is: … 1. Extract all potential matching orders [i.e. determining a target order corresponding to each service content in the order to obtain a target order set, wherein a target order corresponding to a service content is an order comprising the service content] at best price from the order book into a list … Sort the list by explicit time priority, oldest timestamp first [i.e. and has the latest generation time]. This is the matching list … Find the ‘Matching volume’, which is the total volume of all the orders in the matching list … Find the ‘tradable volume’, which is the smallest of the matching volume and the volume left to trade on the incoming order … Allocate volume to each order in the matching list in turn, starting at the beginning of the list” [0168-0173] and “the action associated with the transaction is determined. For example, it may be determined whether the incoming transaction comprises an order to buy or sell a quantity of the associated financial instrument [i.e. wherein a target order corresponding to a service content is an order comprising the service content and has the latest generation time] or an order to modify or cancel an existing order in the electronic market” [0115]);
-determining, for each service content in the order, whether the target order set comprises the target order corresponding to the service content (Bonig, see at least: “the action associated with the transaction is determined. For example, it may be determined whether the incoming transaction comprises an order to buy or sell a quantity of the associated financial instrument [i.e. determining, for each service content in the order, whether the target order set comprises the target order corresponding to the service content] or an order to modify or cancel an existing order in the electronic market” [0115]); and
-in response to determining that the target order set comprises the target order corresponding to the service content, adding the target order corresponding to the service content to the preceding order set of the order, and adding the order to a succeeding order set of the target order corresponding to the service content (Bonig, see at least: “the action associated with the transaction is determined. For example, it may be determined whether the incoming transaction comprises an order to buy or sell a quantity of the associated financial instrument [i.e. in response to determining that the target order set comprises the target order corresponding to the service content] or an order to modify or cancel an existing order in the electronic market” [0115] and “Order Level Priority Pro Rata, also referred to as Threshold Pro Rata, is similar to the Price (or ‘Vanilla’) Pro Rata algorithm but has a volume threshold defined … The Threshold Pro Rata sequence of events is: … 1. Extract all potential matching orders [i.e. in response to determining that the target order set comprises the target order corresponding to the service content] at best price from the order book into a list … Sort the list by explicit time priority, oldest timestamp first [i.e. adding the target order corresponding to the service content to the preceding order set of the order, and adding the order to a succeeding order set of the target order corresponding to the service content]. This is the matching list … Find the ‘Matching volume’, which is the total volume of all the orders in the matching list … Find the ‘tradable volume’, which is the smallest of the matching volume and the volume left to trade on the incoming order … Allocate volume to each order in the matching list in turn, starting at the beginning of the list” [0168-0173] and “one of the transaction processors 508 may be configured to be optimized for one type of message (e.g., financial transactions) [i.e. in response to determining that the target order set comprises the target order corresponding to the service content] while another of the transaction processors 508 may be configured to be optimized for another type of message (administrative systems messages). In one embodiment, a transaction processor 508 optimized for a message type prioritizes processing that message type over any other message type” [0310]).
Regarding claim 5, Bonig in view of Baptist teaches the method of claim 3.
Bonig does not explicitly teach the respectively determining, based on the content association information, the backlog influence degrees of the orders in the set of to-be-processed orders, comprising: setting a same initial backlog influence degree for the orders in the set of to-be-processed orders; selecting, from the set of to-be-processed orders, an order having the latest generation time as the target order, and performing update steps as follows: determining a sum of a current backlog influence degree and the initial backlog influence degree of the target order as a comparison value, and performing update sub-steps as follows: selecting an order from the preceding order set of the target order as a to-be-updated order, selecting a maximum value from the current backlog influence degree of the to-be-updated order and the comparison value, and updating the current backlog influence degree of the to-be-updated order using the selected maximum value; determining whether there is an unselected order in the preceding order set; selecting, in response to determining that there is an unselected order in the preceding order set, the unselected order from the preceding order set to continue to perform the update sub-steps; and deleting, in response to determining that there is no unselected order in the preceding order set, the target order from the set of to-be-processed orders, and re-selecting an order having the latest generation time from the updated set of to-be-processed orders as the target order to continue to perform the update steps.
Baptist, however, teaches prioritizing requests (i.e. abstract), including the known technique of the respectively determining, based on the content association information, the backlog influence degrees of the orders in the set of to-be- processed orders, comprising: setting a same initial backlog influence degree for the orders in the set of to-be-processed orders (Baptist, see at least: “The scores (e.g., trust, compliance, billing and level of use) [i.e. wherein the respectively determining, based on the content association information, the backlog influence degrees of the orders in the set of to-be- processed orders, comprises:] may be obtained (e.g., received, generated, etc.) by determining whether a requestor's pending request is a first request to the DSN. When the pending request is the first request, the computing device utilizes a default score for one or more of the trust, compliance, billing and level of use scores [i.e. setting a same initial backlog influence degree for the orders in the set of to-be-processed orders]. When the pending request is not the first request, the computing device retrieves the requestor's one or more scores from memory of the DSN. Note the one or more of the trust, compliance, billing and level of use scores may be weighted, averaged, summed, etc. for calculating the prioritization score. Further note, one or more of the trust, compliance, billing and level of use scores may be used as the prioritization score. Still further note, the scores may be further modified the computing device when in the queue based on a determination by the computing device. For example, when a requestor has two pending requests in queue 95, the computing device may modify (e.g., increase, decrease) scores for one or both of the requests (e.g., when one request is a higher priority, when one request is of significantly smaller size, when one request is for a rebuilt encoded data slice, etc.)” [0045]);
the known technique of selecting, from the set of to-be-processed orders, an order having the latest generation time as the target order (Baptist, see at least: “The scores (e.g., trust, compliance, billing and level of use) [i.e. wherein the respectively determining, based on the content association information, the backlog influence degrees of the orders in the set of to-be- processed orders, comprises:] may be obtained (e.g., received, generated, etc.) by determining whether a requestor's pending request is a first request to the DSN. When the pending request is the first request, the computing device utilizes a default score for one or more of the trust, compliance, billing and level of use scores. When the pending request is not the first request [i.e. selecting, from the set of to-be-processed orders, an order having the latest generation time as the target order], the computing device retrieves the requestor's one or more scores from memory of the DSN. Note the one or more of the trust, compliance, billing and level of use scores may be weighted, averaged, summed, etc. for calculating the prioritization score. Further note, one or more of the trust, compliance, billing and level of use scores may be used as the prioritization score. Still further note, the scores may be further modified the computing device when in the queue based on a determination by the computing device. For example, when a requestor has two pending requests in queue 95, the computing device may modify (e.g., increase, decrease) scores for one or both of the requests (e.g., when one request is a higher priority, when one request is of significantly smaller size, when one request is for a rebuilt encoded data slice, etc.)” [0045]), and performing update steps as follows:
the known technique of determining a sum of a current backlog influence degree and the initial backlog influence degree of the target order as a comparison value (Baptist, see at least: “The scores (e.g., trust, compliance, billing and level of use) [i.e. wherein the respectively determining, based on the content association information, the backlog influence degrees of the orders in the set of to-be- processed orders, comprises:] may be obtained (e.g., received, generated, etc.) by determining whether a requestor's pending request is a first request to the DSN. When the pending request is the first request, the computing device utilizes a default score for one or more of the trust, compliance, billing and level of use scores [i.e. the initial backlog influence degree of the target order]. When the pending request is not the first request, the computing device retrieves the requestor's one or more scores from memory of the DSN. Note the one or more of the trust, compliance, billing and level of use scores may be weighted, averaged, summed, etc. for calculating the prioritization score [i.e. determining a sum of a current backlog influence degree and the initial backlog influence degree of the target order as a comparison value]. Further note, one or more of the trust, compliance, billing and level of use scores may be used as the prioritization score. Still further note, the scores may be further modified the computing device when in the queue based on a determination by the computing device. For example, when a requestor has two pending requests in queue 95, the computing device may modify (e.g., increase, decrease) scores for one or both of the requests (e.g., when one request is a higher priority, when one request is of significantly smaller size, when one request is for a rebuilt encoded data slice, etc.) [i.e. a current backlog influence degree]” [0045] Examiner notes that when the new total sum of the trust, compliance, billing and level of use scores, some of the these scores are the same as the default score and others are updated [i.e. determining a sum of a current backlog influence degree and the initial backlog influence degree of the target order as a comparison value, and performing update sub-steps]), and performing update sub-steps as follows:
the known technique of selecting an order from the preceding order set of the target order as a to-be-updated order, selecting a maximum value from the current backlog influence degree of the to-be-updated order and the comparison value, and updating the current backlog influence degree of the to-be-updated order using the selected maximum value (Baptist, see at least: “The scores (e.g., trust, compliance, billing and level of use) may be obtained (e.g., received, generated, etc.) by determining whether a requestor's pending request is a first request to the DSN. When the pending request is the first request, the computing device utilizes a default score for one or more of the trust, compliance, billing and level of use scores [i.e. selecting an order from the preceding order set of the target order as a to-be-updated order]. When the pending request is not the first request, the computing device retrieves the requestor's one or more scores from memory of the DSN. Note the one or more of the trust, compliance, billing and level of use scores may be weighted, averaged, summed, etc. for calculating the prioritization score. Further note, one or more of the trust, compliance, billing and level of use scores may be used as the prioritization score. Still further note, the scores may be further modified the computing device when in the queue based on a determination by the computing device. For example, when a requestor has two pending requests in queue 95, the computing device may modify (e.g., increase, decrease) scores for one or both of the requests [i.e. selecting an order from the preceding order set of the target order as a to-be-updated order, selecting a maximum value from the current backlog influence degree of the to-be-updated order and the comparison value, and updating the current backlog influence degree of the to-be-updated order using the selected maximum value] (e.g., when one request is a higher priority, when one request is of significantly smaller size, when one request is for a rebuilt encoded data slice, etc.)” [0045]);
the known technique of determining whether there is an unselected order in the preceding order set (Baptist, see at least: “At time period t2 96, the queue 95 now includes, in order of prioritization score, request 2 [i.e. determining whether there is an unselected order in the preceding order set], request 6, request 3, request 4, request 7, and request 5. Note the computing device may re-obtain the prioritization score for one or more requests (e.g., request 2 score increasing from 77 in time t1 to 84 in time t2)” [0047] Examiner notes that request 2 was also in t1 but was not processed [i.e. an unselected order in the preceding order set]);
the known technique of selecting, in response to determining that there is an unselected order in the preceding order set, the unselected order from the preceding order set to continue to perform the update sub-steps (Baptist, see at least: “At time period t2 96, the queue 95 now includes, in order of prioritization score, request 2 [i.e. determining whether there is an unselected order in the preceding order set], request 6, request 3, request 4, request 7, and request 5. Note the computing device may re-obtain the prioritization score for one or more requests (e.g., request 2 score increasing from 77 in time t1 to 84 in time t2) [i.e. selecting, in response to determining that there is an unselected order in the preceding order set, the unselected order from the preceding order set to continue to perform the update sub-steps]” [0047] Examiner notes that request 2 was also in t1 but was not processed [i.e. an unselected order in the preceding order set t]); and
the known technique of deleting, in response to determining that there is no unselected order in the preceding order set, the target order from the set of to-be-processed orders, and re-selecting an order having the latest generation time from the updated set of to-be-processed orders as the target order to continue to perform the update steps (Baptist, see at least: “During time t4 100, the computing device executes requests 5, 10, and 8, deletes requests 5, 10 and 8 from the queue [i.e. deleting, in response to determining that there is no unselected order in the preceding order set, the target order from the set of to-be-processed orders], receives requests 11, 12 and 13 and obtains prioritization scores for requests 11, 12 and 13. At time t5 102, the queue now includes requests 9, 11, 12 and 13 in order of prioritization score [i.e. and re-selecting an order having the latest generation time from the updated set of to-be-processed orders as the target order to continue to perform the update steps]” [0050]). These known techniques are applicable to the method of Bonig as they both share characteristics and capabilities, namely, they are directed to prioritizing requests.
It would have been recognized that applying the known techniques of the respectively determining, based on the content association information, the backlog influence degrees of the orders in the set of to-be-processed orders, comprising: setting a same initial backlog influence degree for the orders in the set of to-be-processed orders; selecting, from the set of to-be-processed orders, an order having the latest generation time as the target order, and performing update steps as follows: determining a sum of a current backlog influence degree and the initial backlog influence degree of the target order as a comparison value, and performing update sub-steps as follows: selecting an order from the preceding order set of the target order as a to-be-updated order, selecting a maximum value from the current backlog influence degree of the to-be-updated order and the comparison value, and updating the current backlog influence degree of the to-be-updated order using the selected maximum value; determining whether there is an unselected order in the preceding order set; selecting, in response to determining that there is an unselected order in the preceding order set, the unselected order from the preceding order set to continue to perform the update sub-steps; and deleting, in response to determining that there is no unselected order in the preceding order set, the target order from the set of to-be-processed orders, and re-selecting an order having the latest generation time from the updated set of to-be-processed orders as the target order to continue to perform the update steps, as taught by Baptist, to the teachings of Bonig would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modifications of the respectively determining, based on the content association information, the backlog influence degrees of the orders in the set of to-be-processed orders, comprising: setting a same initial backlog influence degree for the orders in the set of to-be-processed orders; selecting, from the set of to-be-processed orders, an order having the latest generation time as the target order, and performing update steps as follows: determining a sum of a current backlog influence degree and the initial backlog influence degree of the target order as a comparison value, and performing update sub-steps as follows: selecting an order from the preceding order set of the target order as a to-be-updated order, selecting a maximum value from the current backlog influence degree of the to-be-updated order and the comparison value, and updating the current backlog influence degree of the to-be-updated order using the selected maximum value; determining whether there is an unselected order in the preceding order set; selecting, in response to determining that there is an unselected order in the preceding order set, the unselected order from the preceding order set to continue to perform the update sub-steps; and deleting, in response to determining that there is no unselected order in the preceding order set, the target order from the set of to-be-processed orders, and re-selecting an order having the latest generation time from the updated set of to-be-processed orders as the target order to continue to perform the update steps, as taught by Baptist, into the method of Bonig would have been recognized by those of ordinary skill in the art as resulting in an improved method that would improve the response time for completion of the service (Baptist, [0005]).
Claims 8 and 11-14 recite limitations directed towards an apparatus for processing orders, the apparatus comprising: one or more processors; and a storage apparatus, storing one or more programs thereon, the one or more programs, when executed by the one or more processors, cause the one or more processors to implement operations (Bonig, see at least: “The computer system 200 can include a set of instructions that can be executed to cause the computer system 200 to perform any one or more of the methods or computer based functions disclosed herein. The computer system 200 may operate as a standalone device or may be connected, e.g., using a network, to other computer systems or peripheral devices. Any of the components discussed above, such as the processor 202, may be a computer system 200 or a component in the computer system 200” [0429]). The rest of the limitations recited in claims 8 and 11-14 are parallel in nature to those addressed above for claims 1-5, respectively, and are therefore rejected for those same reasons set forth above in claims 1-5, respectively.
Claims 10 and 17-20 recite limitations directed towards a non-transitory computer readable storage medium, storing a computer program thereon, wherein, the program, when executed by a processor, implements operations (Bonig, see at least: “The operations of computer devices and systems shown in FIG. 1 may be controlled by computer-executable instructions stored on a non-transitory computer-readable medium” [0427] and “The computer system 200 can include a set of instructions that can be executed to cause the computer system 200 to perform any one or more of the methods or computer based functions disclosed herein. The computer system 200 may operate as a standalone device or may be connected, e.g., using a network, to other computer systems or peripheral devices. Any of the components discussed above, such as the processor 202, may be a computer system 200 or a component in the computer system 200” [0429]). The rest of the limitations recited in claims 10 and 17-20 are parallel in nature to those addressed above for claims 1-5, respectively, and are therefore rejected for those same reasons set forth above in claims 1-5, respectively.
Response to Arguments
Rejections under 35 U.S.C. §101
Applicant argues per Step 2A Prong 1 of the Alice/Mayo test, while basic order sorting may be generalized as a business-related activity, the complete combination of limitations recited in amended Claim 1 is far beyond a bare abstract idea or manual business rule. Combined with the content of the present specification, all recited steps are dedicated technical operations tailored for automated electronic order processing systems, and cannot be implemented by manual work (Remarks, pages 1-2).
Examiner respectfully disagrees. The recited limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a), as they encompass advertising, and marketing or sales activities. Examiner notes that any recited additional elements are analyzed under Prong 2 of step 2A not Prong 1. Additionally, even when analyzed under Prong 2, merely automating the recited order processing steps amounts to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea). Accordingly, the claims are directed to an abstract idea.
Applicant further argues that the amended Claim 1 recites: determining a target attribute of a preceding order set of each order in the set of to-be processed orders, wherein the target attribute is used for indicating whether the preceding order set is empty, wherein each order in the set of to-be-processed orders includes a same service content as orders in the preceding order set, respectively, and each order is generated no earlier than the orders in its preceding order set. This limitation is directed to defining a preceding order set and judging its empty state, which is a technical operation for analyzing data dependency between electronic orders. As explicitly recorded in Paragraphs 48-52 of the specification, orders sharing the same service content form associated order groups with sequential generation times. A preceding order set represents the dependent upstream orders of a current order in the electronic system. This data dependency only exists in digitally stored order data of a computer system. Manual sorting cannot automatically identify such digital association relationships between massive orders, nor judge the empty state of a preceding order set based on electronic data attributes. This is a technical judgment for digital data, rather than a general manual business arrangement (Remarks, pages 2-3).
Examiner respectfully disagrees. Merely automating the sorting of orders based on when they are received and what is received before them amounts to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea). Additionally the order of which associated orders are received is not a data dependency that only exists in digitally stored order data of a computer system. Accordingly, the claims are directed to an abstract idea.
Applicant further argues that the amended Claim 1 further recites: determining, based on the target attribute of the preceding order set of each order and the backlog influence degree of each order, a processing sequence of each order, wherein an order having an empty preceding order set is processed earlier than an order having a non-empty preceding order set, and an order having a high backlog influence degree is processed earlier than an order having a low backlog influence degree. The above step determines a processing sequence by combining the attribute of the preceding order set and the backlog influence degree, which is a customized sorting algorithm for computer batch order processing. As stated in Paragraphs 3 8-4 7 of the specification, the backlog influence degree quantifies the risk of cascading congestion caused by a single order in an electronic processing system. The dual-priority rule set forth in the claim is not a conventional manual scheduling rule. It is designed specifically to address the inherent defects of computer batch processing, and cannot be directly applied to manual order management (Remarks, page 3).
Examiner respectfully disagrees. MPEP 2106.05(a) states that “the judicial exception alone cannot provide the improvement.” Sorting the order sequence based on an attribute of the preceding order set and the backlog influence degree falls within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas as its sales activity and does not utilize additional elements in order reduce order congestion. Additionally, reducing order congestion is not a problem that is inherent to computer technology. Accordingly, the claims are directed to an abstract idea and are not integrated into a practical application.
Applicant further argues that the amended Claim 1 also includes the following series of operational steps: selecting an order having an empty preceding order set from the set of to-be-processed orders and adding the selected order to an order processing queue, and performing processing steps as follows: in response to determining that the order processing queue is non-empty and there is a current idle thread, selecting an order having the highest backlog influence degree from the order processing queue as a candidate order, processing the candidate order using the idle thread, and deleting the candidate order from the order processing queue; releasing the occupied thread in response to determining that the processing of the candidate order is completed, and deleting an association relationship between the candidate order and the orders in the succeeding order set of the candidate order; and updating, in response to determining that the set of to-be processed orders comprises an unprocessed order, the order processing queue to continue to perform the processing steps. These are fundamental underlying operations of a computer operating system and server program: Building an order processing queue and selecting tasks based on rules belongs to computer task scheduling; Matching idle threads to execute tasks is the resource allocation logic of multi-threads; Deleting the association relationship between the candidate order and the orders in the succeeding order set is an operation to clean up invalid digital links in the system; Updating the queue realizes cyclic processing of remaining digital orders. All the above operations rely on the hardware and software architecture of a computer, and have no corresponding implementation modes in manual scenarios. In summary, the amended Claim 1 does not recite a standalone abstract idea of "organizing orders." Every limitation quoted above is a concrete technical operation bound to electronic devices and digital data. It does not fall into the category of patent-ineligible abstract ideas under 35 U.S.C. § 101 (Remarks, pages 3-4).
Examiner respectfully disagrees. Building an order processing queue and selecting tasks based on rules, matching idle order threads to execute order processing, deleting the association relationship between the candidate order and the orders in the succeeding order set, and updating the queue are merely organizing orders during order processing (i.e. a sales activities). Again, the judicial exception alone cannot provide the improvement (see MPEP 2106.05(a)). Additionally, task scheduling cyclic processing are not technical improvements, the resource allocation is not achieved via any additional elements, and the claims merely recite deleting an associated relationship, the claims do not recite cleaning up invalid digital links in the system. Furthermore, merely utilizing generic computer components to implement the abstract idea amounts to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea). Accordingly, the claims are directed to an abstract idea and are not integrated into a practical application.
Applicant further argues that, per Step 2A Prong 2 and Step 2B of the Alice/Mayo test, the amended Claim 1 fully integrates the order processing logic into a practical technical application of an electronic order processing system, and contains substantial additional technical features that are "significantly more" than the abstract idea. Combined with the technical problems and beneficial effects elaborated in the specification, the invention achieves tangible improvements to computer system performance, which complies with MPEP 2106 requirements. The complete technical solution recited in amended Claim 1 includes determining the target attribute of a preceding order set, establishing dual sorting rules, and performing queue and thread management operations. As clearly described in Paragraph 55 of the specification, conventional electronic order processing systems generally adopt a simple first-come-first-served mechanism based on generation time. When a large number of associated orders exist in batches, an unprocessed upstream order will block all subsequent dependent orders, resulting in cascading order congestion, low thread utilization and excessive system overhead. These are typical technical defects of computerized order processing, rather than manual management problems. The entire technical solution of Claim 1 is developed to target the above technical pain points. (Remarks, page 4).
Examiner respectfully disagrees. Determining the target attribute of a preceding order set, establishing dual sorting rules, and performing queue and thread management operations are not technical solutions and do not utilize additional elements, as required by MPEP 2106.05(a), to increase thread utilization and lower system overhead. Additionally, order congestion is not a technical problem. Accordingly, the claims are not integrated into a practical application and do not amount to significantly more than an abstract idea.
Applicant further argues that the technical solution consisting of all limitations in amended Claim 1 brings definite, measurable technical improvements to the computer system, which are recorded in the specification (e.g., Paragraph 56, Paragraph 99-109): 1. Eliminates cascading order congestion; 2. Improves utilization of computer multi-thread resources; and 3. Reduces system operating overhead (Remarks, pages 4-5).
Examiner respectfully disagrees. Eliminating cascading order congestion via optimization for the data processing flow, improving utilization of computer multi-thread resources via a scheduling mechanism, and reducing system operating overhead by reducing the amount of data for processing are not technical solutions. Again, the judicial exception alone cannot provide the improvement (see MPEP 2106.05(a)). Accordingly, the claims are not integrated into a practical application and do not amount to significantly more than an abstract idea.
Applicant further argues that the Office Action holds that the claims only use generic computing hardware to implement abstract ideas. The Applicant respectfully disagrees. The amended Claim 1, with all verbatim limitations set forth above, is not a simple automation of manual rules: 1. It designs a data dependency identification mechanism via determining a target attribute of a preceding order set; 2. It creates a custom dual-priority sorting algorithm based on the target attribute of the preceding order set and the backlog influence degree; 3. It optimizes server operations including order queue management, idle thread scheduling, thread release and deletion of order association relationships. All the above are active improvements to the inherent functions of the computer and its operating system, not just using a computer as a passive execution tool. The combination of all recited limitations transforms the basic order processing idea into a dedicated technical solution for electronic systems, which meets the standard of "integrating the judicial exception into a practical application" under MPEP 2106. Taken as a whole, the limitations of amended Claim provide an inventive concept that is significantly more than the abstract idea. The claimed subject matter is a technical improvement for electronic order processing systems, rather than a patent-ineligible abstract idea. (Remarks, pages 5-6).
Examiner respectfully disagrees. Organizing order data via determining a target attribute of a preceding order set, sorting orders based on the target attribute of the preceding order set and the backlog influence degree, as well as, order queue management, idle thread scheduling, thread release and deletion of order association relationships are sales activities that are merely implemented by generic computer components. The recited additional elements are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea). Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually. Accordingly, the claims are not integrated into a practical application and do not amount to significantly more than an abstract idea.
Rejections under 35 U.S.C. §102/103
Applicant argues that Bonig discloses a distributed financial transaction processing system with only basic order receiving, matching and simple priority sorting. It fails to disclose any of item (i), (ii) and (iii), and lacks the foundational mechanism required by the whole solution. Regarding feature (i), Bonig never uses or defines the term preceding order set, nor the paired concept of a succeeding order set; Bonig only groups orders by product category, but does not define any time sequence limitation that an order is generated no earlier than orders in its preceding order set; Bonig has no disclosure of the step to determine whether a preceding order set is empty. In short, Bonig never defines the concept of a preceding order set, nor the associated rules for service content and generation time. It also has no step to determine whether a preceding order set (each order in the set of to-be-processed orders includes a same service content as orders in the preceding order set; and each order is generated no earlier than the orders in its preceding order set) is empty. This foundational order dependency relationship is entirely absent from Bonig (Remarks, pages 6-8).
Examiner respectfully disagrees. Bonig discloses that incoming orders are matched with orders of the same product type and grouped together in an order list that is sorted by time priority with the oldest timestamp being first (see Bonig, [0115], [0127], and [0168-0173]). In other words, the oldest timestamp of the order associated with the product type has an empty proceeding order set as it is the first incoming order. Additionally, a preceding order set are merely orders that precede the currently incoming orders, Bonig defines the concept of a preceding order set as it sorts orders based on their respective timestamps. Accordingly, the cited references teach this argued feature.
Applicant further argues that, regarding feature (ii), the sorting logic in (ii) takes two factors derived from (i) and the backlog influence degree as the joint judgment basis: The primary sorting factor is the empty/non-empty state of the preceding order set defined in (i); The secondary sorting factor is the backlog influence degree. Bonig only uses single indicators such as transaction time or market quality index for sorting. Since Bonig does not have the preceding order set mechanism in (i), it is impossible to implement the dual-dimension sorting rule in (ii) that relies on (i). This combined technical logic is not taught or suggested by Bonig (Remarks, page 8).
Examiner respectfully disagrees. As detailed in response to the argument above, Bonig discloses (i). Additionally, Bonig discloses matching orders with higher Market Quality Indexes (“MQI”), which is a value that indicates a likelihood that the order will increase a probability that subsequent requests and transaction from other market participants will be satisfied [i.e. the backlog influence degree], prior to matching orders with lower MQIs. Accordingly, the cited references teach these argued features.
Applicant further argues that, regarding (iii), the entire queue building, thread scheduling and association relationship management flow in (iii) is premised on selecting orders with an empty preceding order set (a judgment result of (i)): The source of orders for the processing queue is limited to orders with an empty preceding order set, which is determined per the rules in (i); After processing, the step of deleting the association relationship with the succeeding order set also originates from the order dependency defined in (i). Bonig only has simple order book storage, and it does not possess the foundational preceding order set mechanism in (i). Accordingly, Bonig cannot realize the targeted queue selection, thread scheduling and association relationship clearing operations in (iii). In summary, Bonig lacks the foundational feature (i), so it cannot support the implementation of (ii) and (iii). The complete technical solution of amended Claim 1 is not obvious over Bonig alone (Remarks, pages 8-9).
Examiner respectfully disagrees. As detailed in responses to the argument above, Bonig discloses (i) and (ii). Accordingly, the cited references teach these argued features.
Applicant further argues that, even combining Bonig and Baptist, the combined prior art still does not disclose the foundational feature (i). Since (ii) and (iii) are dependent on (i), the combined references cannot realize the claimed technical solution either. Meanwhile, there is no reasonable motivation for a person having ordinary skill in the art (POSITA) to combine the two references. As rebutted above, Bonig's Paragraph [0127] only discloses product classification for trading markets and does not disclose the claimed preceding order set. Baptist, in tum, focuses on general network request scheduling and contains no definition or description of a preceding order set, succeeding order set, service content matching rules, or generation time limitations for associated orders. Neither reference discloses the step of determining whether a preceding order set is empty. Feature (i) is the fundamental premise for the entire technical solution. Without (i), the subsequent steps in (ii) and (iii) lose their technical basis and cannot be executed at all (Remarks, page 9).
Examiner respectfully disagrees. As detailed in responses to the argument above, Bonig discloses (i). Baptist is not cited to teach (i). Accordingly, the cited references teach the argued features.
Applicant further argues that the dual sorting mechanism in (ii) relies on the empty state of the preceding order set from (i). Baptist only discloses general priority scoring for ordinary network requests, and it does not involve the order dependency defined in (i). Neither reference can provide the combined sorting logic based on the preceding order set state plus backlog influence degree. The sorting rule in (ii) is absent from the combined prior art (Remarks, page 9).
Examiner respectfully disagrees. As detailed in responses to the argument above, Bonig discloses (i) and (ii). Baptist is not cited to teach (i) or (ii). Accordingly, the cited references teach the argued features.
Applicant further argues that all operations in (iii) are built upon the judgment result of (i): Selecting orders with an empty preceding order set to form a queue is a unique screening rule derived from (i); Deleting the association relationship between the candidate order and the succeeding order set is also a follow-up operation for the order dependency defined in (i). Baptist merely provides general queue addition, deletion and basic thread management for common network tasks. It has no matching mechanism related to preceding order sets. Therefore, the combined prior art cannot achieve the targeted queue scheduling, thread allocation and association relationship deletion steps in (iii) (Remarks, pages 9-10).
Examiner respectfully disagrees. As detailed in responses to the argument above, Bonig discloses (i). Baptist is not cited to teach (i). Accordingly, the cited references teach the argued features.
Applicant further argues that the cited art documents fail to disclose features i) to iii) recited in each of the independent Claims 1, 8, and 10. For this reason, Claims 1, 8 and 10 are non-obvious under 35 U.S.C. § 103 and thus patentable over the cited arts. Other dependent claims are also patentable: by virtue of their dependencies on the patentable independent Claims 1, 8 and 10, and their inclusion of additional technical features. Accordingly, withdrawal of the rejections under 35 U.S.C. § 103 is respectfully requested (Remarks, page 10).
Examiner respectfully disagrees. As detailed in responses to the argument above, the cited references teach features i) to iii) recited in each of the independent Claims 1, 8, and 10. Accordingly, independent Claims 1, 8, and 10 and their respective dependent claims are not allowable over the prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Zhang et al. (US 2018/0025407 A1) teaches an order allocation method.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689
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