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 .
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 non-statutory subject matter.
Step 1:
According to the first part of the analysis, in the instant case, claims 1-11 are directed to a method, claims 12-19 are directed to using a system to perform the method, and claim 20 are directed to a non-transitory computer readable medium comprising computer code. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Regarding claim 1:
A method comprising:
retrieving using a processor first data events associated with a first device and second data events associated with a second device, wherein the first data events comprise interactions by the first device with a first plurality of websites and the second data events comprise interactions by the second device with a second plurality of websites;
identifying first unstructured data corresponding to the first data events associated with the first device and second unstructured data corresponding to the second data events associated with the second device;
assigning a first high probability keyword to the first device based on first device information and the first unstructured data and a second high probability keyword to the second device based on second device information and the second unstructured data;
generating a first activity estimation parameter for the first device using the first high probability keyword and a second activity estimation parameter for the second device using the second high probability keyword, wherein the first activity estimation parameter is used to estimate a first probability of a first subsequent data event being taken by the first device and the second activity estimation parameter is used to estimate a second probability of the second subsequent data event being taken by the second device.
Step 2A Prong 1:
“retrieving using a processor first data events associated with a first device and second data events associated with a second device, wherein the first data events comprise interactions by the first device with a first plurality of websites and the second data events comprise interactions by the second device with a second plurality of websites” is directed to mental step of data gathering.
“identifying first unstructured data corresponding to the first data events associated with the first device and second unstructured data corresponding to the second data events associated with the second device” is directed to mental step of identifying data.
“assigning a first high probability keyword to the first device based on first device information and the first unstructured data and a second high probability keyword to the second device based on second device information and the second unstructured data” is directed to math because assigning a high probability keyword to a device based on unstructured data is a classic application of Bayesian inference and machine learning algorithms. The term “high probability” implies a likelihood calculation. Processing unstructured data often involves descriptive and inferential statistic to summarize word frequencies or identify patterns that distinguish “specialty words” from random noise.
“generating a first activity estimation parameter for the first device using the first high probability keyword and a second activity estimation parameter for the second device using the second high probability keyword, wherein the first activity estimation parameter is used to estimate a first probability of a first subsequent data event being taken by the first device and the second activity estimation parameter is used to estimate a second probability of the second subsequent data event being taken by the second device” is directed to math because high probability keyword implies a statistical analysis to determine the likelihood of a keyword appearing in a specific context. Activity estimation parameter is a variable in a mathematical formula that quantifies the relationship between the keyword and the behavior. Estimate a probability is directed to application of probability theory. You are taking input data and using a function to output a probability score for a future event. Subsequent data event represent a stochastic (probabilistic) outcome being predicted by a model. In short, this is a predictive modeling pipeline transforming data into parameters, and parameters into probabilities, which is entirely mathematical.
Each limitation recites in the claim is a process that, under BRI covers performance of the limitation in the mind. Nothing in the claim elements precludes the steps from practically being performed in the mind. Thus, the claim recites a mental process.
Further, the claim recites the step of "assigning a first high probability keyword to the first device based on first device information and the first unstructured data and a second high probability keyword to the second device based on second device information and the second unstructured data; generating a first activity estimation parameter for the first device using the first high probability keyword and a second activity estimation parameter for the second device using the second high probability keyword, wherein the first activity estimation parameter is used to estimate a first probability of a first subsequent data event being taken by the first device and the second activity estimation parameter is used to estimate a second probability of the second subsequent data event being taken by the second device” which as drafted, under BRI recites a mathematical calculation. The grouping of "mathematical concepts” in the 2019 PED includes "mathematical calculations" as an exemplar of an abstract idea. 2019 PEG Section |, 84 Fed. Reg. at 52. Thus, the recited limitation falls into the "mathematical concept" grouping of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889.
Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(i) and (iii).
Additional Elements:
Step 2A Prong 2:
“retrieving using a processor first data events associated with a first device and second data events associated with a second device, wherein the first data events comprise interactions by the first device with a first plurality of websites and the second data events comprise interactions by the second device with a second plurality of websites” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“identifying first unstructured data corresponding to the first data events associated with the first device and second unstructured data corresponding to the second data events associated with the second device” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“assigning a first high probability keyword to the first device based on first device information and the first unstructured data and a second high probability keyword to the second device based on second device information and the second unstructured data” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“generating a first activity estimation parameter for the first device using the first high probability keyword and a second activity estimation parameter for the second device using the second high probability keyword, wherein the first activity estimation parameter is used to estimate a first probability of a first subsequent data event being taken by the first device and the second activity estimation parameter is used to estimate a second probability of the second subsequent data event being taken by the second device” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
The claim is merely selecting data, manipulating or analyzing the data using math and mental process, and displaying the results.
This is similar to electric power: MPEP 2106.05(h) vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
The claim as a whole does not meet any of the following criteria to integrate the judicial exception into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Step 2B:
“retrieving using a processor first data events associated with a first device and second data events associated with a second device, wherein the first data events comprise interactions by the first device with a first plurality of websites and the second data events comprise interactions by the second device with a second plurality of websites” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“identifying first unstructured data corresponding to the first data events associated with the first device and second unstructured data corresponding to the second data events associated with the second device” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“assigning a first high probability keyword to the first device based on first device information and the first unstructured data and a second high probability keyword to the second device based on second device information and the second unstructured data” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“generating a first activity estimation parameter for the first device using the first high probability keyword and a second activity estimation parameter for the second device using the second high probability keyword, wherein the first activity estimation parameter is used to estimate a first probability of a first subsequent data event being taken by the first device and the second activity estimation parameter is used to estimate a second probability of the second subsequent data event being taken by the second device” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
The claim is therefore ineligible under 35 USC 101.
Claim 12 is similar to claim 1 but recites a system comprising an interface configured and a processor configured. These additional elements fail to integrate the abstract idea into a practical application. These limitations are recited at a high level of generality and do not add significantly more to the judicial exception. These elements are generic computing devices that perform generic functions. Using generic computer elements to perform an abstract idea does not integrate an abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Moreover, “the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 223; see also FairWarninglP, LLCv. latric SysInc., 839 F.3d 1089, 1096 (Fed. Cir. 2016) (citation omitted) (“[T]he use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter”).
On the record before us, we are not persuaded that the hardware of claim 12 integrates the abstract idea into a practical application. Nor are we persuaded that the additional elements are anything more than well-understood, routine, and conventional so as to impart subject matter eligibility to claim 12.
Claim 20 cites a non-transitory computer-readable medium comprising computer code to perform a method. This amounts to nothing more than instructions to implement the abstract idea on a computer, which fails to integrate the abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Additionally, using instructions to implement an abstract idea on a generic computer “is not ‘enough’ to transform an abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 226. Therefore, the rejection of claim 20 for the same reason discussed above with regard to the rejection of claim 1.
Regarding claims 2 and 13, “wherein a first plurality of high probability keywords are generated and ranked for the first device” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 3 and 14, “wherein generating the first activity estimation parameter includes generating a first composite probability metric based on a combination of conditional probability metrics identifying the first high probability keyword associated with the composite probability metric” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 4 and 15, “wherein first additional data events associated with the first device are received and the first activity estimation parameter is adjusted based on first additional unstructured data associated with first additional data events” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 5 and 16, “wherein a first additional high probability keyword is generated based on first additional unstructured data associated with first additional data events” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 6 and 17, “wherein a first accuracy metric is generated based on the first additional high probability keyword and the first activity estimation parameter” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 7 and 18, “wherein second additional data events associated with the second device are received and the second activity estimation parameter is adjusted based on second additional unstructured data associated with second additional data events” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 8 and 19, “wherein a second additional high probability keyword is generated based on second additional unstructured data associated with second additional data events” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 9, “wherein a second accuracy metric is generated based on the second additional high probability keyword and the second activity estimation parameter” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 10, “wherein the first subsequent data event comprises an interaction between the first device and a first additional website corresponding to the first high probability keyword” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 11, “wherein the second subsequent data event comprises an interaction between the second device and a second additional website corresponding to the second high probability keyword” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Hence the claims 1-20 are treated as ineligible subject matter under 35 U.S.C. § 101.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. See In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970);and, In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent is shown to be commonly owned with this application. See 37 CFR 1.130(b).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-20 are rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claims 1-19 of U.S. Patent No. 11,868,229. Although the conflicting claims are not identical, they are not patentably distinct from each other because claim 1 of prior art anticipate claim 1 of instant application as follows:
US application 18/522,122
1. A method comprising: retrieving using a processor first data events associated with a first device and second data events associated with a second device, wherein the first data events comprise interactions by the first device with a first plurality of websites and the second data events comprise interactions by the second device with a second plurality of websites; identifying first unstructured data corresponding to the first data events associated with the first device and second unstructured data corresponding to the second data events associated with the second device; assigning a first high probability keyword to the first device based on first device information and the first unstructured data and a second high probability keyword to the second device based on second device information and the second unstructured data; generating a first activity estimation parameter for the first device using the first high probability keyword and a second activity estimation parameter for the second device using the second high probability keyword, wherein the first activity estimation parameter is used to estimate a first probability of a first subsequent data event being taken by the first device and the second activity estimation parameter is used to estimate a second probability of the second subsequent data event being taken by the second device.
2. The method of claim 1, wherein a first plurality of high probability keywords are generated and ranked for the first device.
3. The method of claim 2, wherein generating the first activity estimation parameter includes generating a first composite probability metric based on a combination of conditional probability metrics identifying the first high probability keyword associated with the composite probability metric.
4. The method of claim 1, wherein first additional data events associated with the first device are received and the first activity estimation parameter is adjusted based on first additional unstructured data associated with first additional data events.
5. The method of claim 4, wherein a first additional high probability keyword is generated based on first additional unstructured data associated with first additional data events.
6. The method of claim 5, wherein a first accuracy metric is generated based on the first additional high probability keyword and the first activity estimation parameter.
7. The method of claim 1, wherein second additional data events associated with the second device are received and the second activity estimation parameter is adjusted based on second additional unstructured data associated with second additional data events.
8. The method of claim 7, wherein a second additional high probability keyword is generated based on second additional unstructured data associated with second additional data events.
9. The method of claim 8, wherein a second accuracy metric is generated based on the second additional high probability keyword and the second activity estimation parameter.
10. The method of claim 1, wherein the first subsequent data event comprises an interaction between the first device and a first additional website corresponding to the first high probability keyword.
11. The method of claim 1, wherein the second subsequent data event comprises an interaction between the second device and a second additional website corresponding to the second high probability keyword.
12. A system comprising: an interface configured to receive first data events associated with a first device and second data events associated with a second device, wherein the first data events comprise interactions by the first device with a first plurality of websites and the second data events comprise interactions by the second device with a second plurality of websites; a processor configured to identify first unstructured data corresponding to the first data events associated with the first device and second unstructured data corresponding to the second data events associated with the second device, the processor further configured to assigning a first high probability keyword to the first device based on first device information and the first unstructured data and a second high probability keyword to the second device based on second device information and the second unstructured data, wherein a first activity estimation parameter is generated for the first device using the first high probability keyword and a second activity estimation parameter is generated for the second device using the second high probability keyword, wherein the first activity estimation parameter is used to estimate a first probability of a first subsequent data event being taken by the first device and the second activity estimation parameter is used to estimate a second probability of the second subsequent data event being taken by the second device.
13. The system of claim 12, wherein a first plurality of high probability keywords are generated and ranked for the first device.
14. The system of claim 13, wherein generating the first activity estimation parameter includes generating a first composite probability metric based on a combination of conditional probability metrics identifying the first high probability keyword associated with the composite probability metric.
15. The system of claim 12, wherein first additional data events associated with the first device are received and the first activity estimation parameter is adjusted based on first additional unstructured data associated with first additional data events.
16. The system of claim 15, wherein a first additional high probability keyword is generated based on first additional unstructured data associated with first additional data events.
17. The system of claim 16, wherein a first accuracy metric is generated based on the first additional high probability keyword and the first activity estimation parameter.
18. The system of claim 12, wherein second additional data events associated with the second device are received and the second activity estimation parameter is adjusted based on second additional unstructured data associated with second additional data events.
19. The system of claim 18, wherein a second additional high probability keyword is generated based on second additional unstructured data associated with second additional data events.
20. A non-transitory computer readable medium comprising computer code for: retrieving using a processor first data events associated with a first device and second data events associated with a second device, wherein the first data events comprise interactions by the first device with a first plurality of websites and the second data events comprise interactions by the second device with a second plurality of websites; identifying first unstructured data corresponding to the first data events associated with the first device and second unstructured data corresponding to the second data events associated with the second device; assigning a first high probability keyword to the first device based on first device information and the first unstructured data and a second high probability keyword to the second device based on second device information and the second unstructured data; generating a first activity estimation parameter for the first device using the first high probability keyword and a second activity estimation parameter for the second device using the second high probability keyword, wherein the first activity estimation parameter is used to estimate a first probability of a first subsequent data event being taken by the first device and the second activity estimation parameter is used to estimate a second probability of the second subsequent data event being taken by the second device.
US Patent No. 11,868,229
1. A method comprising: retrieving, using one or more processors, data from at least one data source, the data comprising a plurality of data events associated with a plurality of devices; generating, using the one or more processors, a plurality of probability metrics for each of the plurality of devices based on device information and data event parameters included in the retrieved data, wherein generating the plurality of probability metrics includes generating and assigning a first high probability keyword to first unstructured data associated with a first device in the plurality of devices and a second different probability keyword to second unstructured data associated with a second device in the plurality of devices; and generating, using the one or more processors, an activity estimation parameter for each of the plurality of devices based on the plurality of probability metrics, the activity estimation parameter comprising an estimated probability of a subsequent data event being taken by a device.
2. The method of claim 1, wherein the plurality of probability metrics is determined based on a plurality of device identifiers included in the device information, geographical data associated with the plurality of data events, and data event parameters comprising a type of website associated with the plurality of data events.
3. The method of claim 1, wherein the generating of the plurality of probability metrics further comprises: generating a conditional probability metric for each of a plurality of keywords associated with each of the plurality of devices.
4. The method of claim 3, wherein the generating of the activity estimation parameter further comprises: generating a composite probability metric based on a combination of conditional probability metrics; and identifying a keyword associated with the composite probability metric.
5. The method of claim 1 further comprising: receiving an additional data event associated with at least one of the plurality of devices.
6. The method of claim 5 further comprising: generating an accuracy metric based on a comparison of an additional keyword and the activity estimation parameter.
7. The method of claim 6 further comprising: updating training data based on the accuracy metric.
8. The method of claim 1 further comprising: generating a message comprising the activity estimation parameter.
9. The method of claim 8, wherein the message comprises an updated keyword.
10. A system comprising: a communications interface configured to send and receive network traffic; a storage device configured to store data values in a database; and one or more processors configured to: retrieve data from at least one data source, the data comprising a plurality of data events associated with a plurality of devices; generate a plurality of probability metrics for each of the plurality of devices based on device information and data event parameters included in the retrieved data, wherein generating the plurality of probability metrics includes generating and assigning a first high probability keyword to first unstructured data associated with a first device in the plurality of devices and a second different probability keyword to second unstructured data associated with a second device in the plurality of devices; and generate an activity estimation parameter for each of the plurality of devices based on the plurality of probability metrics, the activity estimation parameter comprising an estimated probability of a subsequent data event being taken by a device.
11. The system of claim 10, wherein the plurality of probability metrics is determined based on a plurality of device identifiers included in the device information, geographical data associated with the plurality of data events, and data event parameters comprising a type of website associated with the plurality of data events.
12. The system of claim 10, wherein the generating of the plurality of probability metrics further comprises: generating a conditional probability metric for each of a plurality of keywords associated with each of the plurality of devices.
13. The system of claim 12, wherein the generating of the plurality of probability metrics further comprises: generating a composite probability metric based on a combination of conditional probability metrics; and identifying a keyword associated with the composite probability metric.
14. The system of claim 10, wherein the one or more processors are further configured to: receive an additional data event associated with at least one of the plurality of devices; and generate an accuracy metric based on a comparison of an additional keyword and the activity estimation parameter.
15. The system of claim 14, wherein the one or more processors are further configured to: update training data based on the accuracy metric.
16. A non-transitory computer readable medium embodying a computer program product, said computer program product comprising a non-transitory computer-readable program code capable of being executed by one or more processors, said the program code comprising instructions configurable to cause the one or more processors to perform a method comprising: retrieving, using one or more processors, data from at least one data source, the data comprising a plurality of data events associated with a plurality of devices; generating, using the one or more processors, a plurality of probability metrics for each of the plurality of devices based on device information and data event parameters included in the retrieved data, wherein generating the plurality of probability metrics includes generating and assigning a first high probability keyword to first unstructured data associated with a first device in the plurality of devices and a second different probability keyword to second unstructured data associated with a second device in the plurality of devices; and generating, using the one or more processors, an activity estimation parameter for each of the plurality of devices based on the plurality of probability metrics, the activity estimation parameter comprising an estimated probability of a subsequent data event being taken by a device.
17. The non-transitory computer readable medium of claim 16, wherein the plurality of probability metrics is determined based on a plurality of device identifiers included in the device information, geographical data associated with the plurality of data events, and data event parameters comprising a type of website associated with the plurality of data events.
18. The non-transitory computer readable medium of claim 16, wherein the generating of the plurality of probability metrics further comprises: generating a conditional probability metric for each of a plurality of keywords associated with each of the plurality of devices.
19. The non-transitory computer readable medium of claim 18, wherein the generating of the activity estimation parameter further comprises: generating a composite probability metric based on a combination of conditional probability metrics; and identifying a keyword associated with the composite probability metric.
20. The non-transitory computer readable medium of claim 18, wherein the program code further comprises instructions for: receiving an additional data event associated with at least one of the plurality of devices; generating an accuracy metric based on a comparison of an additional keyword and the activity estimation parameter; and updating training data based on the accuracy metric.
Other Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Taropa et al. (US 10,698,901) disclose a method (methods, systems, and
apparatus, including computer programs encoded on computer storage media, for ranking query completions based on activity session data; abstract; figure 2)
comprising: retrieving, using one or more processors, data from at least one data
source (search system 230 comprises computer programs installed on one or more computers and executed by a processor analyzing a very large collection of activity sessions, describing the activities of many users (from at least one data source), in session database 282; column 5, line 28 through column 6, line 7; column 11, lines 20-53), the data comprising a plurality of data events associated with a plurality of devices (activity sessions describe the activities, including searched queries (data events), of many users using user devices 210; column 5, line 28 through column 6, line 7 and lines 18-46).
Zhao et al. (10,614,483) disclose at block 104, advertisements having keywords that match those of the content page are identified. Each advertisement may contain one or more keywords stored in a computer readable memory. These keywords may be any combination of text in any language. In some implementations, the keywords are words or phrases. In another implementation, the keywords may be a combination of letters and numbers. The keywords may have been selected by an advertiser. In some implementations, the advertiser may have selected the keywords via an online auction process. These advertisements may have been created by any entity and may relate to any product, business, service, etc. These advertisements may include text, an image, video, interactive game, audio, or any other form of communication, and any combination thereof. In some implementations, block 104 may include receiving only a pre-defined group of advertisements that meet a certain criteria. The criteria may be set by the advertisement program, the web page publisher, or the advertiser. The criteria may be the content of the advertisement, the quality of the advertisement, media type, language, demographics, product, service, or any other criteria. In some implementations, the criteria may be defined by age-appropriateness of the material. In another implementation, the group of advertisements may be defined by its pixel display size. The advertisements may be received from a database or any other computer-readable memory via a network (Col.3, lines 12-38).
Sheng et al. (US 8,924,338) disclose a computer-implemented method for automatically updating a tag embedded in a webpage that summarizes a current version of the webpage, the method comprising: extracting, by a computing device, a first model representative of the current version of the webpage, the first model including nodes defining a hierarchy of elements of the current version of the webpage; comparing, by the computing device, the first model with a second model corresponding to a previous version of the webpage to detect one or more changes to the webpage; determining by the computing device, at least one keyword corresponding to the current version of the webpage if the changes exceed a threshold, wherein determining the keyword comprises: extracting an item set of words or phrases from the current version of the webpage; selecting a rule from a trained set of rules based on the item set of extracted words or phrases, the rule defining (1) an association of the item set to a keyword and (2) a property vector that measures quality of the association; and computing a maximum likelihood score based on the property vector corresponding to the selected rule, the maximum likelihood score representing a probability of relationship between the selected rule and the corresponding keyword; comparing, by the computing device, the keyword corresponding to the current version of the webpage with at least one keyword corresponding to the previous version of the webpage; and updating, by the computing device, the tag of the webpage to include the keyword corresponding to the current version depending on the comparison and the likelihood score.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN H LE whose telephone number is (571)272-2275. The examiner can normally be reached on Monday-Friday from 7:00am – 3:30pm Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN H LE/Primary Examiner, Art Unit 2857