DETAILED ACTION
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 .
Introduction
The following is a final Office Action in response to Applicant’s communications received on October 8, 2025. Claims 1, 8 and 15 have been amended.
Currently claims 1-20 are pending. Claims 1, 8 and 15 are independent.
Response to Amendments
Applicant’s amendments necessitated the new ground(s) of rejection in this Office Action.
Applicant’s amendments to claims 1, 8 and 15 are NOT sufficient to overcome the 35 U.S.C. § 101 rejection as set forth in the previous Office Action. Therefore, the 35 U.S.C. § 101 rejection to claims 1-20 has been maintained.
Response to Arguments
Applicant’s arguments filed on October 8, 2025 have been fully considered but are not persuasive.
In the Remarks on page 10, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that the amendments obviate the §101 rejections.
In response to Applicant’s argument, the Examiner respectfully notes that the amendments are in the right direction but are not sufficient to overcome the 35 U.S.C. § 101 rejection as the machine learning model needs to be retrained/updated with the confirmed/feedback data in order to consider as an improvement to the functioning of the machine learning model each time when executed by the processor.
In the Remarks on page 12, Applicant argues that neither Alspaugh, Badger nor Palay, alone or in combination, teaches or suggest all of the claimed features. For example, claim 1 recites, among other things, “training a machine learning model using training data generated by transforming event data instances and attribution scores to predict attribution likelihood between event data instances”, and “applying the trained machine learning model to event data instances received after training, to calculated an attribution score….”.
Applicant’s arguments have been fully considered; however, Applicant’s arguments are directed to the newly amended claims, and therefore, the newly amended claims will be fully addressed in this Office Action.
Claim Rejections – 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. The added subject matter which is not in the original specification is as follows:
Claims 1, 8 and 15 recite a “the machine learning model configured to predict attribution likelihood”. The newly added limitations appear to constitute new matter. Paragraphs [0049]-[0050] describe, at best, “the attribution unit can be used as a private data set for training a machine learning algorithm…the attribution unit 314 can identify mistakes and errors in the data…, can identify potential inputs and feature relationships that are most relevant to a customer’s task, and the attribution unit 314 can transform the data to be appropriate for generating predictions.” “the machine learning algorithm can identifying fraudulent user activity, the machine learning algorithm can configured to determine whether any of the anomalies and/or contradictions are indicative of fraudulent activity…”, but nothing in the specification mentions about “predict attribution likelihood between event data instances. Applicant is required to cancel or amend the claims in accordance with written description requirements.
Dependent claims 2-7, 9-14 and 16-20 are also rejected for the same reasons as each depends on the rejected claims.
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 an abstract idea without significantly more.
As per Step 1 of the subject matter eligibility analysis, it is to determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claims 1-7 are directed to a method for estimating a downstream impact, which falls within the statutory category of a process; claims 8-14 are directed to an apparatus comprising a processor and computer-readable medium, which falls within the statutory category of a machine; and Claims 15-20 are directed to a non-transitory computer-readable medium storing instructions, which falls within the statutory category of a product.
In Step 2A of the subject matter eligibility analysis, it is to “determine whether the claim at issue is directed to a judicial exception (i.e., an abstract idea, a law of nature, or a natural phenomenon). Under this step, a two-prong inquiry will be performed to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance), then determine if the claim recites additional elements that integrate the exception into a practical application of the exception. See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 Guidance), 84 Fed. Reg. 50, 54-55 (January 7, 2019).
In Prong One, it is to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance, a law of nature, or a natural phenomenon).
Taking the method as representative, claim 1 recites steps of “transmitting a data schema, receiving a first event data instance and a second event data instance, formatting the first event data instance and the second event data instance to conform to a uniform format as described by the data schema, receiving attribution criteria specifying rules for determining attribution between event data instance, training a machine learning model using training data to predict attribution likelihood, calculate an attribution score, identifying a common characteristic between the first and second event data instance characteristics, linking the first data event data instance and the second event data instance based at least in part on a third event data instance characteristic of the plurality of event data instance characteristic of the data schema, adding a data field to a table associated with the first event data instance, storing a memory address for the second event data instance in the data field of the table, storing the linked list of associated events, receiving call from a third party requesting the attribution score, and transmitting attribution score to the third party”. None of the limitations recites technological implementation details for any of these steps, but instead recite only results desired by any and all possible means. Training a machine learning model is no more than training a generic model using a collection of functions and data without significant details and retraining the machine learning model with optimized training data in order to improve the functioning of the machine learning model. The limitations, as drafted, are methods that allow user to manipulate data requested by a thirty-party, manage commercial interactions including marketing or sales activities or behaviors, and manage personal behavior or interactions between people including social activities, teaching and following rules or instruction, which fall within the certain methods of organizing human activity grouping. See 2019 Revised Guidance, 84 Fed. Reg. 52. Further, the claim recites a concept similar to the claims as discussed in Electric Power Group (e.g., collecting information, analyzing it, and displaying certain result of the collection and analysis, see Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1351-52, 119 USPQ2d 1739, 1740 (Fed. Cir. 2016)).
Dependent claims 2-7 further narrow the limitations of claim 1 which also cover subject matter that is judicially excepted from patent eligibility under § 101.
The mere nominal recitation of “by a computing device”, “a web server”, and “an application programming interface” do not take the claim out of the certain methods of organizing human activity grouping because these elements are recited at a high level of generality amounted to no more than generic computer components for generic computer functions. See 2019 Revised Guidance, 84 Fed. Reg. 52. Further, the “memory address”, in this case, is directed to data per se. Accordingly, the claims recites an abstract idea. The analysis is proceeding to Prong Two.
In Prong Two, it is to determine if the claim recites additional elements that integrate the exception into a practical application of the exception.
Beyond the abstract idea, the claims recite the additional elements of “by a computing device”, “a web server”, “a machine learning model”, and “an application programming interface”. The Specification discloses these additional elements at a high level of generality, for example, “The computing device can transmit a data schema to an application executing on a web server; The computing device can receive a first event data instance and a second event data instance from the first client application” (see Abstract; ¶ 39). When given the broadest reasonable interpretation and in light of the Specification, these additional elements are no more than generic computer components. Thus, merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014); and Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (A computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims.”). Further, reciting “a machine learning model” is merely adding the words “apply it” or using “a particular machine” with an abstract idea, or mere instructions to implement the abstract idea on a computer. The Supreme Court has repeatedly made clear that merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract. See Affinity Labs of Texas, LLC v. DirecTV, LLC, 838 F.3d 1253, 1258 (Fed. Cir. 2016). As to learning per se, such an argument overlooks the entire education system. Reciting machine learning is placing such learning in a computer context, offering no technological implementation details beyond the conceptual idea to use a machine for learning. However, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, and does not reflect an improvement to the functioning of a computer itself or another technology. Therefore, the additional elements do not integrate the judicial exception into a practical application. The claims are directed to an abstract idea, the analysis is proceeding to Step 2B.
In Step 2B of Alice, it is "a search for an ‘inventive concept’—i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept’ itself.’” Id. (alternation in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1294 (2012)).
The claims as described in Prong Two above, nothing in the claims that integrates the abstract idea into a practical application. The same analysis applies here in Step 2B.
Claim 1 recites the additional elements of “by a computing device”, “a web server”, “a machine learning model” and “an application programming interface”. The Specification discloses these additional elements at a high level of generality, for example, “The computing device can transmit a data schema to a first client application executing on a first web server; The computing device can receive a first event data instance and a second event data instance from the first client application” (see Abstract; ¶ 39). When given the broadest reasonable interpretation and in light of the Specification, these additional elements are no more than generic computer components. At best, these generic computer components may perform the generic computer functions including receiving, storing, and transmitting information over a network. However, generic computer for performing generic computer functions have been recognized by the courts as merely well-understood, routine, and conventional functions of generic computers. See MPEP 2106.05 (d) (II) (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); Collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1351-52, 119 USPQ2d 1739, 1740 (Fed. Cir. 2016); and Storing a memory address in an appropriate field of the associated entry in the table (see Palay et al., US_5813120, col. 15, lines 1-6)). Thus, simply implementing the abstract idea on a generic computer for performing generic computer functions do not amount to significantly more than the abstract idea. (MPEP 2106.05(a)-(c), (e-f) & (h)).
For the foregoing reasons, claims 1-7 cover subject matter that is judicially-excepted from patent eligibility under § 101 as discussed above, the other claims, system claims 8-14 and medium claims 15-20 parallel claims 1-7—similarly cover claimed subject matter that is judicially excepted from patent eligibility under § 101.
Therefore, the claims as a whole, viewed individually and as a combination, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims are not patent eligible.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Alspaugh et al., (US 10936643 B1, hereinafter: Alspaugh), and in view of Badger et al., (US 2021/0209637, hereinafter: Badger), and further in view of Palay et al., (US 5613120, hereinafter: Palay), and Yee et al., (US 2023/0042210, hereinafter: Yee).
Regarding claim 1, Alspaugh discloses a computer-implemented method comprising:
transmitting, by a computing device of a cloud computing system (see col. 7, lines 33-44), a data schema to a first client application executing on a first web server, the data schema describing a plurality of event data instance characteristics (see col. 4, lines 20-52, col. 8, lines 43-67, claim 1, claim 2);
receiving, by the computing device, a first event data instance and a second event data instance from the first client application, the first event data instance comprising a first event data instance characteristic of the plurality of event data instance characteristics, and the second event data instance comprising a second event data instance characteristic of the plurality of event data instance characteristics (see col. 6, lines 43-67, col. 64, line 61 to col. 65, line 62, claim 21, and claim 24);
formatting, by the computing device, the first event data instance and the second event data instance to conform to a uniform format as described by the data schema (see col. 4, line 53 to col. 5, line 9, col. 5, line 57 to col. 6, line 21, col. 15, lines 43-55), the data schema comprising at least one of a customer ID (see Fig. 7A; col. 33, lines 54 to col. 34, line 15), a request ID, or a device ID;
receiving, by the computing device, attribution criteria specifying rules for determining attribution between event data instances (see col. 4, line 53 to col 5, line 40; col. 23, line 53 to col. 24, line 32);
receiving, by the computing device, an application programming interface call from a third party requesting the attribution score between the first event data instance and the second event data instance (see col. 8, lines 26-42, col. 9, lines 20-34, col. 13, lines 35-64, col. 17, lines 39-55); and
transmitting, by the computing device, the attribution score between the first event data instance and the second event data instance to the third party based at least in part on the application programming interface call (see col. 30, lines 4-43, claims 13-15).
Alspaugh discloses generating a score for each of the lower level instances associated with a cluster that represents the probability, or the confidence that the lower level instance properly belongs in the associated higher level cluster. Such score may indicate some measure of the strength of the association between the lower and higher instances (see col. 66, lines 48-60, and col. 75, lines 21-40).
Alspaugh does not explicitly disclose the following limitations; however, Badger in an analogous art for attributing purchase events between online and offline activity discloses
the data schema comprising at least a request ID or a device ID (see ¶ 28);
applying, by the computing device, the trained machine learning model to event data instances received after training, to calculate an attribution score between the first event data instance and the second event data instance based at least in part on the attribution criteria and outputs of the trained machine learning model, the attribution score representing a strength of association between the first and second event data instance (see ¶ 42, ¶ 52, ¶ 60-61, claim 28);
identifying, by the computing device, a common characteristic between the first event data instance characteristic and the second event data instance characteristic based at least in part on the data schema and based at least in part on the attribution score (see ¶ 18, ¶ 27-28, ¶ 52-53);
linking, by the computing device and based at least in part on the identified common characteristic, the first event data instance and the second event data instance to create or modify a linked list of associated event (see ¶ 28-29, ¶ 35, ¶ 38, ¶ 42, ¶ 53-54).
.It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alspaugh to include teaching of Badger in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing the score computation efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Alspaugh and Badger do not explicitly disclose the following limitations; however, Palay in an analogous art for linking source files discloses
adding a data field to a table associated with the first event data instance (see col. 4, lines 7-39; col. 15, line 56 to col. 16, line 6); and
storing a memory address for the second event data instance in the data field of the table associated with the first event data in instance (see col. 15, lines 1-6, and lines 46-55);
storing the linked list of associated events in a memory queue (see col. 6, lines 15-21; col. 8, lines 16-28).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alspaugh and in view of Badger to include teaching of Palay in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution for data management, which improves the efficiency of object-oriented computation. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Alspaugh discloses the task intelligence harvester may implement a machine learning (ML) or artificial intelligence (AI) processing to identify patterns of task occurrences that correlated to other data (see col. 64, lines 42-60); and Badger discloses calculating a attribution score representing the strength of the attribution between the identified activity and purchase events (see ¶ 60-61).
Alspaugh, Badger and Palay do not explicitly disclose the following limitations; however, Yee in an analogous art for data aggregation discloses
training, by the computing device, a machine learning model using training data generated by transforming event data instances and attribution scores, the machine learning model configured to predict attribution likelihood between event data instances (see ¶ 5-7, ¶ 57-59);
applying, by the computing device, the trained machine learning model to event data instances received after training (see ¶ 5, ¶ 69, ¶ 98).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alspaugh and in view of Badger and palay to substitute the feature of Yee in order to gain the commonly understood benefit of such adaption, such as providing the benefit of using AI model for more accurate future event prediction. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2, Alspaugh discloses the computer-implemented method of claim 1, wherein the first event data instance corresponds to a first request by a first client application to the web server, and the second event data instance corresponds to a second request from a second client application to the web server (see col. 4, lines 20-52, col. 7, lines 17-31; col. 12, lines 3-36, claim 9).
Regarding claim 3, Alspaugh discloses the computer-implemented method of claim 1, wherein the first event data instance is received by the computing device in a first format and the second event data instance is received by the computing device in a second format (see col. 4, line 45 to col. 5, line 9, col. 15, lines 53-55).
Regarding claim 4, Alspaugh discloses the computer-implemented method of claim 1, wherein the memory address comprises a pointer (see col. 20, lines 5-15, col. 34, lines 37-52).
Regarding claim 5, Alspaugh does not explicitly disclose the following limitations; however Badger discloses the computer-implemented method of claim 1, wherein the method further comprises transmitting the linked first event data instance and second event data instance to a memory queue prior to calculating the attribution score (see ¶ 29-30, ¶ 52-53). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alspaugh to include teaching of Badger in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing the score computation efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 6, Alspaugh discloses the computer-implemented method of claim 1, wherein the method further comprises, upon the calculation of the attribution score, formatting the linked first event data instance and second event data instance for use as training data for a machine learning algorithm (see col. 52, lines 35-63, col. 64, lines 42-60).
Regarding claim 7, Alspaugh discloses the computer-implemented method of claim 1, wherein the linked first event data instance and second event data instance comprise a linked list (see Fig. 8A; col. 23, lines 34-52, col. 38, lines 9-21, col. 40, line 65 to col. 41, line 3).
Regarding claim 8, Alspaugh discloses a cloud infrastructure node, comprising:
a processor (see col. 6, lines 31-42); and
a computer-readable medium including instructions that (see col. 78, lines 54-67), when executed by the processor, cause the processor to:
transmit a data schema to a first client application executing on a first web server, the data schema describing a plurality of event data instance characteristics (see col. 4, lines 20-52, col. 8, lines 43-67, claim 1, claim 2);
receive a first event data instance and a second event data instance from the first client application, the first event data instance comprising a first event data instance characteristic of the plurality of event data instance characteristics, and the second event data instance comprising a second event data instance characteristic of the plurality of event data instance characteristics (see col. 6, lines 43-67, col. 64, line 61 to col. 65, line 62, claim 21, and claim 24);
format the first event data instance and the second event data instance to conform to a uniform format as described by the data schema (see col. 4, line 53 to col. 5, line 9, col. 5, line 57 to col. 6, line 21, col. 15, lines 43-55), the data schema comprising at least one of a customer ID (see Fig. 7A; col. 33, lines 54 to col. 34, line 15), a request ID, or a device ID;
receive attribution criteria specifying rules for determining attribution between event data instances (see col. 4, line 53 to col 5, line 40; col. 23, line 53 to col. 24, line 32);
receive an application programming interface call from a third party requesting the attribution score between the first event data instance and the second event data instance (see col. 8, lines 26-42, col. 9, lines 20-34, col. 13, lines 35-64, col. 17, lines 39-55); and
transmit the attribution score between the first event data instance and the second event data instance to the third party based at least in part on the application programming interface call (see col. 30, lines 4-43, claims 13-15).
Alspaugh discloses generating a score for each of the lower level instances associated with a cluster that represents the probability, or the confidence that the lower level instance properly belongs in the associated higher level cluster. Such score may indicate some measure of the strength of the association between the lower and higher instances (see col. 66, lines 48-60, and col. 75, lines 21-40).
Alspaugh does not explicitly disclose the following limitations; however, Badger in an analogous art for attributing purchase events between online and offline activity discloses
the data schema comprising at least one of a request ID or a device ID (see ¶ 28);
applying, by the computing device, the trained machine learning model to event data instances received after training, to calculate an attribution score between the first event data instance and the second event data instance based at least in part on the attribution criteria and outputs of the trained machine learning model, the attribution score representing a strength of association between the first and second event data instance (see ¶ 42, ¶ 52, ¶ 60-61, claim 28);
identify a common characteristic between the first event data instance characteristic and the second event data instance characteristic based at least in part on the data schema (see ¶ 18, ¶ 27-28, ¶ 52-53);
link, based at least in part on the identified common characteristic, the first event data instance and the second event data instance to create or modify a linked list of associated event (see ¶ 28-29, ¶ 35, ¶ 38, ¶ 42, ¶ 53-54).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alspaugh to include teaching of Badger in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing the score computation efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Alspaugh and Badger do not explicitly disclose the following limitations; however, Palay in an analogous art for linking source files discloses
adding a data field to a table associated with the first event data instance (see col. 4, lines 7-39; col. 15, line 56 to col. 16, line 6); and
storing a memory address for the second event data instance in the data field of the table associated with the first event data in instance (see col. 15, lines 1-6, and lines 46-55);
storing the linked list of associated events in a memory queue (see col. 6, lines 15-21; col. 8, lines 16-28).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alspaugh and in view of Badger to include teaching of Palay in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution for data management, which improves the efficiency of object-oriented computation. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Alspaugh discloses the task intelligence harvester may implement a machine learning (ML) or artificial intelligence (AI) processing to identify patterns of task occurrences that correlated to other data (see col. 64, lines 42-60); and Badger discloses calculating a attribution score representing the strength of the attribution between the identified activity and purchase events (see ¶ 60-61).
Alspaugh, Badger and Palay do not explicitly disclose the following limitations; however, Yee in an analogous art for data aggregation discloses
train a machine learning model using training data generated by transforming event data instances and attribution scores, the machine learning model configured to predict attribution likelihood between event data instances (see ¶ 5-7, ¶ 57-59);
apply the trained machine learning model to event data instances received after training (see ¶ 5, ¶ 69, ¶ 98).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alspaugh and in view of Badger and palay to substitute the feature of Yee in order to gain the commonly understood benefit of such adaption, such as providing the benefit of using AI model for more accurate future event prediction. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 9, Alspaugh discloses the cloud infrastructure node of claim 8, wherein the first event data instance corresponds to a first request by a first client application to the web server, and the second event data instance corresponds to a second request from a second client application to the web server (see col. 4, lines 20-52, col. 7, lines 17-31, col. 12, lines 3-36 and claim 9).
Regarding claim 10, Alspaugh discloses the cloud infrastructure node of claim 8, wherein the first event data instance is received in a first format and the second event data instance is received in a second format (see col. 4, line 45 to col. 5, line 9, col. 15, lines 53-55).
Regarding claim 11, Alspaugh discloses the cloud infrastructure node of claim 8, wherein the memory address comprises a pointer (see col. 20, lines 5-15, col. 34, lines 37-52).
Regarding claim 12, Alspaugh does not explicitly disclose the following limitations; however Badger discloses the cloud infrastructure node of claim 8, wherein the processor further 78 transmits the linked first event data instance and second event data instance to a memory queue prior to calculating the attribution score (see ¶ 29-30, ¶ 52-53). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alspaugh to include teaching of Badger in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing the score computation efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 13, Alspaugh discloses the cloud infrastructure node of claim 8, wherein the processor further, upon the calculation of the attribution score, formats the linked first event data instance and second event data instance for use as training data for a machine learning algorithm (see col. 52, lines 35-63, col. 64, lines 42-60).
Regarding claim 14, Alspaugh discloses the cloud infrastructure node of claim 8, wherein the linked first event data instance and second event data instance comprise a linked list (see Fig. 8A; col. 23, lines 34-52, col. 38, lines 9-21, col. 40, line 65 to col. 41, line 3).
Regarding claim 15, Alspaugh discloses a non-transitory computer-readable medium having stored thereon a sequence of instructions which, when executed by a processor, causes the processor to perform operations comprising:
transmitting a data schema to a first client application executing on a first web server, the data schema describing a plurality of event data instance characteristics (see col. 4, lines 20-52, col. 8, lines 43-67, claim 1, claim 2);
receiving a first event data instance and a second event data instance from the first client application, the first event data instance comprising a first event data instance characteristic of the plurality of event data instance characteristics, and the second event data instance comprising a second event data instance characteristic of the plurality of event data instance characteristics (see col. 6, lines 43-67, col. 64, line 61 to col. 65, line 62, claim 21, and claim 24);
formatting the first event data instance and the second event data instance to conform to a uniform format as described by the data schema (see col. 4, line 53 to col. 5, line 9, col. 5, line 57 to col. 6, line 21, col. 15, lines 43-55), the data schema comprising at least one of a customer ID (see Fig. 7A; col. 33, lines 54 to col. 34, line 15), a request ID, or a device ID;
receiving an application programming interface call from a third party requesting the attribution score between the first event data instance and the second event data instance(see col. 8, lines 26-42, col. 9, lines 20-34, col. 13, lines 35-64, col. 17, lines 39-55); and
transmitting the attribution score between the first event data instance and the second event data instance to the third party based at least in part on the application programming interface call (see col. 30, lines 4-43, claims 13-15).
Alspaugh discloses generating a score for each of the lower level instances associated with a cluster that represents the probability, or the confidence that the lower level instance properly belongs in the associated higher level cluster. Such score may indicate some measure of the strength of the association between the lower and higher instances (see col. 66, lines 48-60, and col. 75, lines 21-40).
Alspaugh does not explicitly disclose the following limitations; however, Badger in an analogous art for attributing purchase events between online and offline activity discloses
the data schema comprising at least one of a request ID or a device ID (see ¶ 28);
applying the trained machine learning model to event data instances received after training, to calculate an attribution score between the first event data instance and the second event data instance based at least in part on the attribution criteria and outputs of the trained machine learning model, the attribution score representing a strength of association between the first and second event data instance (see ¶ 42, ¶ 52, ¶ 60-61, claim 28);
identifying a common characteristic between the first event data instance characteristic and the second event data instance characteristic based at least in part on the data schema and based at least in part on the attribution score (see ¶ 18, ¶ 27-28, ¶ 52-53);
linking, based at least in part on the identified common characteristic, the first event data instance and the second event data instance to create or modify a linked list of associated event (see ¶ 28-29, ¶ 35, ¶ 38, ¶ 42, ¶ 53-54).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alspaugh to include teaching of Badger in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing the score computation efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Alspaugh and Badger do not explicitly disclose the following limitations; however, Palay in an analogous art for linking source files discloses
adding a data field to a table associated with the first event data instance (see col. 4, lines 7-39; col. 15, line 56 to col. 16, line 6); and
storing a memory address for the second event data instance in the data field of the table associated with the first event data in instance (see col. 15, lines 1-6, and lines 46-55);
storing the linked list of associated events in a memory queue (see col. 6, lines 15-21; col. 8, lines 16-28).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alspaugh and in view of Badger to include teaching of Palay in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution for data management, which improves the efficiency of object-oriented computation. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Alspaugh discloses the task intelligence harvester may implement a machine learning (ML) or artificial intelligence (AI) processing to identify patterns of task occurrences that correlated to other data (see col. 64, lines 42-60); and Badger discloses calculating a attribution score representing the strength of the attribution between the identified activity and purchase events (see ¶ 60-61).
Alspaugh, Badger and Palay do not explicitly disclose the following limitations; however, Yee in an analogous art for data aggregation discloses
training a machine learning model using training data generated by transforming event data instances and attribution scores, the machine learning model configured to predict attribution likelihood between event data instances (see ¶ 5-7, ¶ 57-59);
applying the trained machine learning model to event data instances received after training (see ¶ 5, ¶ 69, ¶ 98).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alspaugh and in view of Badger and palay to substitute the feature of Yee in order to gain the commonly understood benefit of such adaption, such as providing the benefit of using AI model for more accurate future event prediction. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 16, Alspaugh discloses the non-transitory computer-readable medium of claim 15, wherein the first event data instance corresponds to a first request by a first client application to the web server, and the second event data instance corresponds to a second request from a second client application to the web server (see col. 4, lines 20-52, col. 7, lines 17-31; col. 12, lines 3-36, and claim 9).
Regarding claim 17, Alspaugh discloses the non-transitory computer-readable medium of claim 15, wherein the first event data instance is received in a first format and the second event data instance is received in a second format (see col. 4, line 45 to col. 5, line 9, col. 15, lines 53-55).
Regarding claim 18, Alspaugh discloses the non-transitory computer-readable medium of claim 15, wherein the memory address comprises a pointer (see col. 20, lines 5-15, col. 34, lines 37-52).
Regarding claim 19, Alspaugh does not explicitly disclose the following limitations; however Badger discloses the non-transitory computer-readable medium of claim 15, wherein the operations further comprise transmitting the linked first event data instance and second event data instance to a memory queue prior to calculating the attribution score (see ¶ 29-30, ¶ 52-53). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alspaugh to include teaching of Badger in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing the score computation efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 20, Alspaugh discloses the non-transitory computer-readable medium of claim 15, wherein the operations further comprise, upon the calculation of the attribution score, formatting the linked first event data instance and second event data instance for use as training data for a machine learning algorithm (see col. 52, lines 35-63, col. 64, lines 42-60).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hiraoka et al., (JP 2006031209) discloses a method for calculating an attribute score from the attribute of the registered document and a composition score from an adaptation score corresponding to the document of the retrieval result.
Dane et al., (CN 107637086) discloses a method for managing distributed discrete content fragment sets of a first event data and a second event data by determining participant scores based on the set of event instances.
Buibas et al., (US 2021/0138430) discloses a system for calculating a confidence score of shopping carts based on the selected sensor data from in an automated store.
Sharma (US 2018/0157699) discloses a method for determining schema change in events received by a data stream processing system a plurality of events generated by a first device and a second device.
Christidis et al., “Different Ways of Framing Event Attribution Questions: The Example of Warm and Wet Winters in the United Kingdom Similar to 2015/16”, Met Office Hadley Centre, Exeter, United Kingdom, 15 June 2018.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/PAN G CHOY/Primary Examiner, Art Unit 3624
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