The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Notice to Applicant
In response to the communication received on 07/02/2026, the following is a Final Office Action for Application No. 18159751.
Status of Claims
Claims 1-20 are pending.
Response to Amendments
Applicant’s amendments have been fully considered. Examiner maintains the Claim Interpretation that provides and interpretation of the claim language that incorporates the non-transitory structure in the specification with the claimed computer readable storage medium.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot in light of the new grounds of rejection, as necessitated by amendment.
As per the 101 rejection, Applicant argues that the claims are in favor of eligibility per Prong One of Step 2A, however Examiner respectfully disagrees. Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Since the recitation of the claims falls into at least one of the above Groupings, there is a basis for providing further analysis with regard to Prong Two of Step 2A to determine whether the recitation of an abstract idea is deduced to being directed to an abstract idea. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Prong Two of Step 2A, however Examiner respectfully disagrees. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The processor and/or memory medium is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic processor server limitation is no more than mere instructions to apply the exception using a generic computer component. Further, processor and/or memory medium to inter alia perform the function of facilitating knowledge trading among the plurality of expert agents by giving access to portions of a dataset is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. In other words, the present claims use a generic processing device and memory medium to inter alia perform the function of facilitating knowledge trading among the plurality of expert agents by giving access to portions of a dataset which is a concept that can be performed in the human mind. The processor is merely used to perform the function(s), and the processor does not integrate the abstract idea into a practical application since there are no meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Step 2B, however Examiner respectfully disagrees. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: processor and/or memory medium. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, processor and/or memory medium to inter alia perform the function of facilitating knowledge trading among the plurality of expert agents by giving access to portions of a dataset is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include the non-limiting or non-exclusive examples of MPEP § 2106.05. Thus, the rejection is maintained.
In an effort to further expedite prosecution, see: July 2024 Subject Matter Eligibility Examples, Example 47. Anomaly Detection. Per the analysis of claim 2 Example 47, the analysis refers to MPEP 2106.05(f) which provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Although the additional elements, e.g. (per Example 47) “using a trained ANN”, limits the identified judicial exceptions, e.g. (per Example 47) “detecting one or more anomalies in a data set using the trained ANN” and, e.g. (per Example 47) “analyzing the one or more detected anomalies using the trained ANN to generate anomaly data,” this type of limitation merely confines the use of the abstract idea to a particular technological environment, e.g. (per Example 47: neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). As an exemplary direction for claim limitations to be eligible, see claims 1 and 3 of Example 47.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder, e.g. computer readable storage medium, that is coupled with functional language, e.g. having program instructions embodied therewith, without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: computer readable storage medium having program instructions embodied therewith in claim 15 and dependent claims. Further, the specification at ¶0019 states that “Some known types of storage devices that include these mediums include: diskette, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded device (such as punch cards or pits / lands formed in a major surface of a disc)…A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se”.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims fall within statutory class of process or machine or manufacture; hence, the claims fall under statutory category of Step 1.
Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
1. A computer-implemented method for collaborative exchange, the method performed by a collaborative prediction platform, the collaborative prediction platform comprising an aggregator/orchestrator agent, the method comprising: publishing a prediction task and key performance indicator (KPI) information; receiving, at a certain time, a plurality of prediction results, each of the plurality of prediction results being produced by one of a plurality of participants expert agents with one of a private model and private data association associated with the one of the plurality of participants expert agents, the private data being stored in a respective private cloud; calculating, for each of the plurality of participants expert agents, a trade score based on the KPI information and the plurality of prediction results for each of the plurality of participants expert agents; determining dynamic agent reputation scores for the plurality of expert agents based on the KPI information and the plurality of prediction results; determining a dynamic pricing of data/features and determining an acquirement score for the one of the private model and the private data associated with each of the plurality of participants expert agents using the corresponding trade score; and publishing the acquirement score for trading the one of the private model and the private data among the plurality of participants expert agents; and facilitating knowledge trading among the plurality of expert agents by giving access to portions of a privately owned dataset to another expert agent to enhance accuracy of predictions.
[or]
8. A system comprising: a memory comprising computer readable instructions; and a processing device for executing the computer readable instructions, the computer readable instructions controlling the processing device to perform operations, by a collaborative prediction platform comprising an aggregator/orchestrator agent, for collaborative exchange, the operations comprising: publishing a prediction task and key performance indicator (KPI) information; receiving, at a certain time, a plurality of prediction results, each of the plurality of prediction results being produced by one of a plurality of participants expert agents with one of a private model and private data association associated with the one of the plurality of participants expert agent, the private data being stored in a respective private cloud; calculating, for each of the plurality of participants expert agents, a trade score based on the KPI information and the plurality of prediction results for each of the plurality of participants expert agents; determining dynamic agent reputation scores for the plurality of expert agents based on the KPI information and the plurality of prediction results; determining dynamic pricing of data/features and determining an acquirement score for the one of the private model and the private data associated with each of the plurality of participants expert agents using the corresponding trade score; and publishing the acquirement score for trading the one of the private model and the private data among the plurality of participants expert agents; and facilitating knowledge trading among the plurality of expert agents by giving access to portions of a privately owned dataset to another expert agent to enhance accuracy of predictions.
[or]
15. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations, by a collaborative prediction platform comprising an aggregator/orchestrator agent, comprising: publishing a prediction task and key performance indicator (KPI) information; receiving, at a certain time, a plurality of prediction results, each of the plurality of prediction results being produced by one of a plurality of participants expert agents with one of a private model and private data association associated with the one of the plurality of participants expert agent, the private data being stored in a respective private cloud; calculating, for each of the plurality of participants expert agents, a trade score based on the KPI information and the plurality of prediction results for each of the plurality of participants expert agents; determining dynamic agent reputation scores for the plurality of expert agents based on the KPI information and the plurality of prediction results; determining dynamic pricing of data/features and determining an acquirement score for the one of the private model and the private data associated with each of the plurality of participants expert agents using the corresponding trade score; and publishing the acquirement score for trading the one of the private model and the private data among the plurality of participants expert agents; and facilitating knowledge trading among the plurality of expert agents by giving access to portions of a privately owned dataset to another expert agent to enhance accuracy of predictions.
The claim(s) recite(s) the following summarization of the abstract idea which includes collaborative exchange executed by the additional element(s) of memory medium and/or processor. This falls into at least the Abstract Idea Grouping of Mental Processes since the information can be analyzed by an abstract evaluation judgment process. Thus, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity since the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion).
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion).
Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The memory medium and/or processing device is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic memory medium and/or processing device limitation is no more than mere instructions to apply the exception using a generic computer component. Further, publishing the acquirement score by a memory medium and/or processing device is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B.
Per Step 2B, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: memory medium and processing device. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, publishing the acquirement score by a memory medium and/or processing device is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure at ¶0020 wherein “computer 101 includes processor set 110 (including processing circuitry 120 and cache 121).” Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
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ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
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iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
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v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine/manufacture for performing the present claims); and receiving or transmitting data (e.g., the present claims).
The dependent claims do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101.
Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Taylor et al. (US 20200364791 A1) hereinafter referred to as Taylor in view of Buhrmann et al. (US 20190294965 A1) hereinafter referred to as Buhrmann.
Taylor teaches:
Claim 1. A computer-implemented method for collaborative exchange, the method performed by a collaborative prediction platform, the collaborative prediction platform comprising an aggregator/orchestrator agent, the method comprising (¶0040 FIG. 8 shows an example data aggregate stage consuming the output of multiple independent pipelines. A signal generate stage is optionally interposed between processing steps by the data aggregate stage. Another signal generate stage is interposed between the data aggregate stage and a transmit stage and it operates in parallel to transmitting messages output from the data aggregate stage to downstream consumers):
publishing a prediction task and key performance indicator (KPI) information (¶0091 The data receive stage offloads the signal generate stage from the computing tasks of receiving data from a data stream. Exemplary tasks include performing network protocol stack processing such as copying a packet into a buffer, verifying the correctness of packet contents using a Cyclic Redundancy Check (CRC), resequencing packets that arrive out of order, and requesting retransmissions of missing packets from the sender. The data receive stage may also parse the input data stream to extract specific data fields that are required by the signal generate stage. The data receive stage may sub-divide its work into pipeline stages, as well as parallel pipelines, in order to maximize its throughput. ¶0142 An automated model quality monitoring system produces daily performance reports to validate the efficacy of the estimators against production data, also called forward testing. Live models are monitored for decay or for material change in any key performance indicators (KPI). Each KPI allows for either objective or subjective determination of performance decay or material change. In the example of FIG. 29B, the decision to retrain is a human decision, but this may be automated. As the model is re-trained with additional, newly available labeled data, the performance accuracy of the model can improve over time.);
receiving, at a certain time, a plurality of prediction results, each of the plurality of prediction results being produced by one of a plurality of expert agents with one of a private model and private data associated with the expert agents, the private data being stored in a respective private cloud (¶0091 The data receive stage offloads the signal generate stage from the computing tasks of receiving data from a data stream. Exemplary tasks include performing network protocol stack processing such as copying a packet into a buffer, verifying the correctness of packet contents using a Cyclic Redundancy Check (CRC), resequencing packets that arrive out of order, and requesting retransmissions of missing packets from the sender. The data receive stage may also parse the input data stream to extract specific data fields that are required by the signal generate stage. The data receive stage may sub-divide its work into pipeline stages, as well as parallel pipelines, in order to maximize its throughput. ¶0235 We will now discuss the relationship between signal accuracy and “opportunity capture” (which is also known as recall). Signal accuracy is the percentage of predictions that are correct, while opportunity capture is the percentage of price durations in the market that are correctly predicted as short or long fuse … For example, consider a series of 40 quote price updates, 10 of which are short fuse and 30 of which are long fuse. Of the 10 short fuse quotes, assume the Quote Fuse signal predicts six short fuse quotes. Of the 30 long fuse quotes, assume the Quote Fuse signal predicts two short fuse quotes. Accuracy and opportunity capture of short fuse quotes for this example would be 80% (6 of 8) and 60% (6 of 10), respectively. Accuracy and opportunity capture of long fuse quotes for this example would be 87.5% (28 of 32) and 93% (28 of 30), respectively. ¶0115 Appending signal data to market data messages does mean that data consumers will be utilizing market data that flows through the signal generating device. There are a number of reasons why this may not be desirable. Market data infrastructure is typically viewed as mission-critical by trading firms. Firms may be interested in incorporating new trading signals into their trading strategies without disrupting their existing market data infrastructure. Similarly, vendors producing new trading signals want to minimize the obstacles to delivering new signals to customers. Requiring trading firms to swap out their market data infrastructure may slow down or eliminate opportunities to win new customers. Accordingly, a technical solution is desirable where trading signals can be integrated into the trading firm's existing market data infrastructure with minimal disruption and modifications of such market data infrastructure.);
calculating, for each of the plurality of participants, a trade score based on the KPI information and the plurality of prediction results for each of the plurality of expert agents (¶0139 Step 4, Model Assessment The resulting model solution is applied to out-of-sample historical market data, where the out-of-sample data are forward in time versus training data. For example, the training data can be sampled from a selected time period (e.g., August-November 2018) whereas the testing data comes from any date after this time period. The scored output of test data is benchmarked against key performance indicators such as precision, recall, F1, Brier scores, Matthews Correlation Coefficient, etc. A model that passes out-of-sample testing undergoes back testing. Back testing applies the model to many years of historical market data to ascertain its efficacy over multiple market cycles. The back testing dates can encompass both training and testing dates. ¶0356 we wondered about the variability in purse value of each symbol on a per trade (or per share) basis. We note that a symbol like SPY that typically trades with a penny spread has an expected price change of one penny increments, whereas a high priced stock like Berkshire Hathaway can have a significantly larger price change between sequential NBBO quotes and trade events. Since the purse is calculated as the quote price difference (delta) multiplied by the size of the trade, we can calculate a new metric: “Purse per Share” (PPS). PPS can be calculated for each trade and averaged daily for each symbol. For each symbol in our test universe, FIG. 46 plots PPS relative to its average spread traded over one day. Accurately predicting the direction of the next NBBO price change can deliver extraordinary improvements to a wide variety of trading strategies.);
determining dynamic agent reputation scores for the plurality of expert agents based on the KPI information and the plurality of prediction results (¶0142 The model solution is deployed into production usage as an estimator in a signal generator using an embodiment as described herein. An automated model quality monitoring system produces daily performance reports to validate the efficacy of the estimators against production data, also called forward testing. Live models are monitored for decay or for material change in any key performance indicators (KPI). Each KPI allows for either objective or subjective determination of performance decay or material change. In the example of FIG. 29B, the decision to retrain is a human decision, but this may be automated. As the model is re-trained with additional, newly available labeled data, the performance accuracy of the model can improve over time);
determining a dynamic pricing of data/features and determining an acquirement score for the one of the private model and the private data associated with each of the plurality of expert agents using the corresponding trade score (¶0239 We have designed an example embodiment for the Quote Fuse signal to serve as an optimizing input to market makers, execution algorithms, smart order routers, and order management systems. We envision multiple scenarios where the ability to assess the duration of NBBO prices can be leveraged for better trading results: Matching—Optimize matching engine performance with a proactive view of route-away requirements and future movements of mid-point pricing in dark pools and alternative trading systems (ATSs) [0241] Sweeping—Enable aggressive execution strategies that minimize market impact by selecting either dark or lit venues based on the stability of NBBO pricing. ¶0371 As shown in FIG. 47, the example Tier 2/3 trading application can use predictive models that consume derivative signal summaries along with market data, historical market data, and other internal signals. Note that the market data in this example may be provided by a market data feed consolidator that uses centralized or regional infrastructure to aggregate and distribute market data at slower speeds and lower cost to Tier 2 and 3 market participants. The predictive model drives trading logic that places buy and sell orders for financial instruments at lower frequency than Tier 1 trading applications.); and
publishing the acquirement score for trading the one of the private model and the private data among the plurality of expert agents (¶0372 As discussed above, the ability to generate derivative trading signals from the real-time, low latency, trading signals provides significant technical benefits in the form of dramatically reduced latency. As discussed above for the use of case of detecting the trading activities of natural investors, the conventional approach has been to source Form 13F and Form 4 regulatory filings via computerized searches of the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system of the United States Securities and Exchange Commission (SEC) to glean so-called “smart money” movements by large, natural investors);
facilitating knowledge trading among the plurality of expert agents by giving access to portions of a privately owned dataset to another expert agent to enhance accuracy of predictions (¶0364 Yet another example use case is for matching engines—which can attract liquidity with AI-driven order types. An operator of trading venue (exchange, dark pool, or ATS) attracts more order flow by providing order types that improve execution quality. The matching engine also wins more direct market access business by offering order types that incorporate tactical trading logic. A discretionary pegged order can use Quote Vector predictions of NBBO price movements to manage the magnitude of the discretionary offset. Instead of mitigating slippage, the matching engine's customers consistently receive price improvements).
Although not explicitly taught by Taylor, Buhrmann teaches in the analogous art of systems using artificial intelligence to analyze natural language sources based on personally-developed intelligent agent models:
determining dynamic agent reputation scores for the plurality of expert agents based on the KPI information and the plurality of prediction results (¶0007 a method for creating an intelligent agent model for providing an expert opinion on source data, the method comprises the steps of obtaining at least one first data source for training an intelligent agent model, receiving training input on the at least one first data source from an individual, extracting a set of relevant attributes of the at least one first data source based on the received training input, weighting the set of relevant attributes based on at least one of the group of the received training input and prior received training input, forming at least one neural network that comprises the intelligent agent model based on the set of relevant attributes and the weighting, ranking or scoring of at least one section of a second data source using the intelligent agent model, and outputting the ranking or scoring. ¶0017 FIG. 12 illustrates an example process for training and using an intelligent model to score a data source, which may be performed by the system of FIG. 2. ¶0020 In some aspects, multiple models may be created and used to analyze the same source data or document. This may provide much more useful information to the user of the system to enable more effective and useful feedback to the data source. The feedback may take the form of changes to improve the data source or document based on an expert opinion. In yet some aspects, an intelligent agent model may be created and then provided through a marketplace or trading interface for use by others. This may take the form of an online market place, or a software as a service arrangement where certain expertise may be provided based on need. In this example, an interface may be provided, such as a web based interface, software as a service, etc., to enable multiple individuals to provide feedback on data sources to encode their expertise into a portable intelligent agent model. The intelligent agent model may then be uploaded to a central system (e.g., cloud-based system) for eventual access and use by others (e.g., users of a market place). ¶0059 Each document, for example corresponding to a profile, may be analyzed using AI to evaluate the quality of each document or data source. The documents/data sources may be scrubbed or cleaned to remove noisy data, for example using AI, or known techniques. The entry point of candidate information extraction and noise removal for this pipeline is depicted in 1101. Certain documents may then be combined into Talent Vaults. A Talent Vault is a logical entity representation of data, as opposed to a document-based representation. The notion of an employee, for example, is defined within the system by a collection of resumes, projects, metadata, correspondence, etc. All of these documents contain various data points and each point may be more or less important to the profile of that employee. The described system may build a dynamic profile of an entity from available data related to or describing that entity, and then apply one or more intelligent agent models to the dynamic profile to determine what pieces of data are significant to the analysis at hand. A “Vault” is a collection of these entity representations; in this case, a “Talent Vault” describes talent related entities.);
determining a dynamic pricing of data/features and facilitating knowledge trading among the plurality of expert agents by giving access to portions of a privately owned dataset to another expert agent to enhance accuracy of predictions (¶0020 In some aspects, multiple models may be created and used to analyze the same source data or document. This may provide much more useful information to the user of the system to enable more effective and useful feedback to the data source. The feedback may take the form of changes to improve the data source or document based on an expert opinion. In yet some aspects, an intelligent agent model may be created and then provided through a marketplace or trading interface for use by others. This may take the form of an online market place, or a software as a service arrangement where certain expertise may be provided based on need. In this example, an interface may be provided, such as a web based interface, software as a service, etc., to enable multiple individuals to provide feedback on data sources to encode their expertise into a portable intelligent agent model. The intelligent agent model may then be uploaded to a central system (e.g., cloud-based system) for eventual access and use by others (e.g., users of a market place).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the systems using artificial intelligence to analyze natural language sources based on personally-developed intelligent agent models of Buhrmann with the system for systems for low latency generation and distribution of trading signals from financial market data of Taylor for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Taylor ¶0030 teaches that it is desirable to utilize predictive machine learning models to improve their performance and/or broaden their utility;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Taylor teaches computing trading signals with low latency and high throughput using highly parallelized compute resources such as integrated circuits, reconfigurable logic devices, graphics processor units (GPUs), multi-core general purpose processors, and/or chip multi-processors (CMPs), and Buhrmann teaches creating an intelligent agent model for providing an expert opinion on source data; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Taylor at least the above cited paragraphs, and Buhrmann at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the systems using artificial intelligence to analyze natural language sources based on personally-developed intelligent agent models of Buhrmann with the system for systems for low latency generation and distribution of trading signals from financial market data of Taylor. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Taylor teaches:
Claim 2. The computer-implemented method of claim 1, further comprising facilitating exchange of the one of the private model and the private data (¶0364 Yet another example use case is for matching engines—which can attract liquidity with AI-driven order types. An operator of trading venue (exchange, dark pool, or ATS) attracts more order flow by providing order types that improve execution quality. The matching engine also wins more direct market access business by offering order types that incorporate tactical trading logic. ¶0109 When transacting trades, exchanges, dark pools, and Alternative Trading Systems (ATS) must either match or improve those prices or route the orders away to a market with a superior price. Similarly, arbitrage trading strategies must be able to identify superior and inferior prices across multiple markets in order to identify profitable trading opportunities. The above-referenced and incorporated U.S. Pat. Nos. 7,921,046 and 8,768,805 describe how market data messages can be aggregated and enhanced, such as by computing aggregate pricing views such as the NBBO at high speed and throughput, as well as further explanations of their value. (See also the above-referenced and incorporated U.S. Pat. No. 10,229,453). Note that the computation of aggregate pricing views and the value of fast computations and predictive signals applies to any fungible financial instrument that trades across multiple markets. Examples include currencies (i.e. the FX market), some fixed income instruments such as on-the-run treasury bonds, as well as stocks and options in the United States, Canada, Europe, and Japan.).
Taylor teaches:
Claim 3. The computer-implemented method of claim 1, wherein the receiving, calculating, determining, and publishing are repeatedly executed (¶0174 When the exposed size is filled, the next chunk of the reserve order is exposed; and this process repeats itself until the full size of the reserve order has been filled (or the remainder of the order has been canceled). The size of each chunk may be fixed or variably sized to further disguise the presence of the reserve order (e.g., by randomizing the sizes of each exposed chunk).).
Taylor teaches:
Claim 4. The computer-implemented method of claim 1, further comprising: calculating, for each expert agent of a plurality of expert agents, a reputation score by using historic accuracies of past predictions by the expert agent; and publishing the reputation score (¶0140 The resulting model solution is applied to out-of-sample historical market data, where the out-of-sample data are forward in time versus training data. For example, the training data can be sampled from a selected time period (e.g., August-November 2018) whereas the testing data comes from any date after this time period. The scored output of test data is benchmarked against key performance indicators such as precision, recall, F1, Brier scores, Matthews Correlation Coefficient, etc. A model that passes out-of-sample testing undergoes back testing. Back testing applies the model to many years of historical market data to ascertain its efficacy over multiple market cycles. The back testing dates can encompass both training and testing dates.).
Taylor teaches:
Claim 5. The computer-implemented method of claim 4, wherein the trade score is further based at least in part on the reputation score (¶0140 The resulting model solution is applied to out-of-sample historical market data, where the out-of-sample data are forward in time versus training data. For example, the training data can be sampled from a selected time period (e.g., August-November 2018) whereas the testing data comes from any date after this time period. The scored output of test data is benchmarked against key performance indicators such as precision, recall, F1, Brier scores, Matthews Correlation Coefficient, etc. A model that passes out-of-sample testing undergoes back testing. Back testing applies the model to many years of historical market data to ascertain its efficacy over multiple market cycles. The back testing dates can encompass both training and testing dates.).
Taylor teaches:
Claim 6. The computer-implemented method of claim 1, further comprising: aggregating the plurality of prediction results for a requestor of the prediction task; and allocating returns to the plurality of expert agents (¶0359 Another example use case is for proprietary traders—who can allocated capital more profitably. For example, a proprietary trader using mid-frequency or high-frequency strategies needs to allocate capital to the most profitable trading opportunities. The Quote Vector signal allows the trader to select liquid names with large Purse-per-Share (PPS) opportunity. For example, consider Stock A and B that are priced at $18.50 and $14.75, respectively, and Stock A and B have PPS values are $0.03 and $0.01, respectively. While Stock A requires 8.5% more capital commitment per share, the trader can capture 3× more profit per share by using the Quote Vector signal. With limits on available capital to trade, the trader chooses to trade Stock A, making the most of Quote Vector's ability to drive profitable trades).
Taylor teaches:
Claim 7. The computer-implemented method of claim 1, wherein at least a portion of the one of the private model and the private data is encrypted (¶0206 As discussed above, an example of an estimator trading signal that can be generated by embodiments disclosed herein is an estimate that is indicative of the size of a detected reserve order, which can be referred to as a hidden liquidity size estimation. Thus, if the purpose of the liquidity indictor trading signal discussed above is to signal market conditions that beget concentrations of execution activity, then the hidden liquidity size estimation trading signal can serve as a useful companion signal that enables customers to respond to the liquidity indicator trading signal with high efficacy and low risk. As discussed above, FIG. 29A shows example processing logic for computing an estimate of the size of a detected reserve order. AI and ML techniques can be used for computing such estimates, such as supervised learning as shown in FIG. 29B to develop a model that estimates the probability of a detected reserve order having a particular size).
As per claims 8-14 and 15-20, the system and manufacture tracks the method of claims 1-7 and 1-6, respectively, resulting in substantially similar limitations. The same cited prior art and rationale of claims 1-7 and 1-6 are applied to claims 8-14 and 15-20, respectively. Taylor discloses that the embodiment may be found as a system and manufacture (Fig. 50 and ¶0130).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KURTIS GILLS whose telephone number is (571)270-3315. The examiner can normally be reached on M-F 8-5 PM.
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/KURTIS GILLS/Primary Examiner, Art Unit 3624
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