Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of the Application
The following is a non-Final Office Action.
In response to Examiner's communication of 7/25/2025, Applicant responded on 10/23/2025. Amended claims 1, 3, 4, 8, 10, 11, 15, and 17. Cancelled claims 5, 6, 12, 13, 18, and 19.
Claims 1-4, 7-11, 14-17, and 20 are pending in this application and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/23/2025 has been entered.
Response to Amendment
Applicant's amendments to claims 1, 3, 4, 8, 10, 11, 15, and 17 are sufficient to overcome the claim objection set forth in the previous action.
Applicant's amendments to claims 1, 3, 4, 8, 10, 11, 15, and 17 are not sufficient to overcome the 35 USC 101 set forth in the previous action.
Applicant's amendments to claims 1, 3, 4, 8, 10, 11, 15, and 17 are sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “...These claims define the presently claimed invention as one in which multiple test messages are tied to portions of a web site using cookies and each message is presented to an independent group of recipients, delivered by the web site for comparison to a baseline message treatment. Calculations are performed using an allocated memory space to improve performance, so that a confidence sequence can be continuously updated as responses are detected, and the confidence sequence is displayed as updated. The display can be scrolled for comfortable viewing while the confidence sequence is updated in real time. These features represent significantly more than an abstract idea. The claims define a graphical interface invention that provides a real-time display for evaluation of messaging treatments segregated for view by different viewers of a web site while testing is in progress.…Applicant's amended independent claims do not recite human activities, actions that can be taken by a human, a mental process, or mathematical concepts. The independent claims, as amended, are directed to "allocating storage" for computations and dynamically displaying, while updating over time, "a confidence sequence from the allocated storage" wherein a display device is configured to be scrollable while simultaneously updating the confidence sequence. At least these operations require complex electrical signaling. Addressing computer memory and controlling a display device each require complex electrical signaling and circuitry that a human mind cannot replicate. With respect to the other amendments, modern web sites are a complex organization of interconnected pages and links. Applicant's amended claims recite the use of cookies to cause different test messages to be presented to groups of recipients on the same web site as part of a test. According to the independent claims, the responses to the different messages are automatically compared to a baseline with respect to a response, confidence values are continuously updated as responses are detected, and the confidence values are displayed as updated. The display can be scrolled for comfortable viewing while confidence values are simultaneously updated in real time. The use of cookies to test a web site with different messages while using the display technique discussed above is not an abstract idea, or, in the alternative, is another practical application of any alleged abstract idea. Applicant's claims, as amended, do not recite mental steps…Applicant's claims for the display of continuously updated, live scrollable values in a confidence sequence provides a technical solution to a problem that is unique in real- time evaluation of the effects of a messaging treatment. As discussed in the aforementioned interview, Applicant's claims, as amended, recite the allocation to storage for the computations with respect to the baseline message treatment. When a different unique test message is provided to different groups of recipients there will be multiple p- values at any given time since these are calculated with respect to a treatment that is a baseline treatment. The confidence sequence is computed for each treatment effect. Confidence bounds are used to estimate a sampling distribution for each treatment effect to restrict and normalize the variance and p-values. The above collection of calculations is computationally intensive and is handled expeditiously enough to provide the real-time display through the use of the allocated storage. See, for example, paragraph [0043] of Applicant's specification. The claims therefore recite a technical solution to a problem that is unique in its field, making them similar from an eligible subject matter perspective to those in BASCOM Global Internet Services v. AT&TMobility, 827 F. 3d 1341. Applicant's claimed invention solves the problem of reviewing test results while testing is in progress, where the results of large numbers of tests need to be compared, necessarily requiring scrolling through results on a display device. A user of a system according to Applicant's claims, can scroll as necessary without delays in the displayed values being updated…Applicant's amended independent claims include displaying the confidence sequence for each of multiple test messages in visual alignment on a display device while the confidence sequence is being updated. The display device is configured to be scrollable while simultaneously updating the values in the confidence sequence. A user can continuously review current confidence values as well as additional information, even while continuously scrolling through large numbers of test results that are displayed as part of the sequence…Dependent claims 3 and 10, as amended, are patent-eligible for an additional reason. These claims now recite, "automatically publishing the selected test message to an expanded group of recipients." (Emphasis added.) These claims recite that the message with the best result is automatically published to an expanded group of participants. Once a test message is determined to be the most successful when compared to a baseline, it automatically becomes the new baseline, to be published via the web site to a larger group of users…Automatic publication of an updated web site is an additional practical application of the claimed invention….”. The Examiner respectfully disagrees.
While Applicant’s amendments further prosecution, unlike BASCOM, the claims and the argued elements in the present application, are directed to, …multiple test messages are automatically compared to a baseline with respect to a response, confidence values are continuously updated as responses are detected…, which is a problem directed to organizing human activity (i.e. humans testing marketing message effectiveness to other humans using statistical and mathematical concepts), a mental process (i.e. humans evaluating and judging marketing message effectiveness to other humans using statistical and mathematical concepts), mathematical concepts (i.e. humans using statistical and mathematical concepts to hypothesize and test marketing messages), as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem or necessarily roots in computing technologies. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer, generic graphical user interface, testing and publishing web sites, tracking humans and human activities with cookies, performing extra solution activities. Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 or amount to significantly more in Step 2B.
The limitations are abstract elements that are part of and directed to the recited abstract idea as described above with respect to the first prong of Step 2A, i.e. mental process, organizing human activities and mathematical concepts, generally linked to a technical environment, i.e. computer, generic graphical user interface, testing and publishing web sites, tracking humans and human activities with cookies. Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
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). MPEP 2106.05(f).
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered.
The closest prior art are US Patent Publication to U.S. Publication No. 2017/0323329 ("Katariya") in view of U.S. Patent Application 2017/0124487 ("Szeto").
However, the teachings of the references do not teach the specific ordered sequence of limitations of independent claims 1, 8, 15.
Claim 1:
determining portions of a web site to provide a plurality of test messages, customizing the portions of the web site to create the plurality of test messages by producing varied portions of the web site corresponding to each of the test messages;
publishing the varied portions of the web site for a specified period, wherein each of the varied portions of the web site corresponds to a cookie, allocating storage for computations with respect to a baseline message treatment,
assaying, over time, using a response module and the cookie, a metric corresponding to a message response from an independent group of recipients for each of the test messages;
deriving, iteratively over time, using a difference module and the storage, a comparative difference between an assayed value of the metric for the message response and a baseline value of the metric corresponding to the baseline message treatment;
estimating, iteratively over time, using a variance module, a variance of an average of the metric for the plurality of test messages;
calculating a current confidence value using a confidence module, iteratively over time to produce a confidence sequence in the allocated storage, the current confidence value based on the variance and an error-corrected p-value normalized within confidence bounds, wherein the current confidence value corresponds to a current difference value for the comparative difference; and
dynamically displaying, while updating over time and using an interface module, the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a display device, wherein the display device is configured to be scrollable while simultaneously updating the confidence sequence.
Claim 8:
determining portions of a web site to provide a plurality of test messages, customizing the portions of the web site to create the plurality of test messages by producing varied portions of the web site corresponding to each of the test messages, wherein each of the varied portions of the web site corresponds to a cookie, allocating storage for computations with respect to a baseline message treatment, and dynamically displaying a confidence sequence:
a server configured to publish the varied portions of the web site for a specified period, a response module configured to assay, over time, using the cookie, a metric corresponding to a message response from an independent group of recipients for each of the test messages;
a difference module configured to derive, iteratively over time, using the storage, a comparative lift for an assayed value of the metric for the message response relative a baseline value of the metric corresponding to the baseline message treatment;
a variance module configured to estimate, iteratively over time, a variance of an average of the metric for the plurality of test messages;
a confidence module configured to calculate, iteratively over time to produce a confidence sequence in the allocated storage, a current confidence value based on the variance and an error-corrected p-value, the current confidence value to produce the confidence sequence; and
an interface module to, while updating over time, dynamically display the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a display device wherein the display device is configured to be scrollable while simultaneously updating the confidence sequence.
Claim 15:
determining portions of a web site to provide a plurality of test messages;
customizing the portions of the web site to create the plurality of test messages by producing varied portions of the web site corresponding to each of the test messages;
publishing the varied portions of the web site for a specified period, wherein each of the varied portions of the web site corresponds to a cookie;
assaying, over time, using the cookie, a metric corresponding to a message response from an independent group of recipients for each of the test messages;
a step for producing, iteratively over time, a current confidence value to produce a confidence sequence in the allocated storage, the current confidence value corresponding to a current difference value for a comparative difference between an assayed value of the metric for the message response and a baseline value of the metric; and
dynamically displaying, while updating over time and using an interface module, the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a display device, wherein the display device is configured to be scrollable while simultaneously updating the confidence sequence.
Furthermore, Non-Patent Literature, “Analyzing and Testing Viewability Methods in an Advertising Network” to Exposito-Ventura et al, 7/9/2020, hereinafter Exposito-Ventura discloses, —Many of the current online businesses base completely their revenue models in earnings from online advertisement. A problematic fact is that according to recent studies more than half of display ads are not being detected as viewable. The International Advertising Bureau (IAB) has defined a viewable impression as an impression that at least 50% of its pixels are rendered in the viewport during at least one continuous second. Although there is agreement on this definition for measuring viewable impressions in the industry, there is no systematic methodologies on how it should be implemented or the trustworthiness of these methods. In fact, the Media Rating Council (MRC) announced that there are inconsistencies across multiple reports attempting to measure this metric. In order to understand the magnitude of the problem, we conduct an analysis of different methods to track viewable impressions. Then, we test a subset of geometric and strong interaction methods in a webpage registered in the worldwide ad-network ExoClick, which currently serves over 7 billion geo-targeted ads a day to a global network of 65000 web/mobile publisher platforms. We find that the Intersection Observer API is the method that detects more viewable impressions given its robustness towards the technological constraints that face the rest of implementations available. The motivation of this work is to better understand the limitations and advantages of such methods, which can have an impact at a standardisation level in online advertising industry, as well as to provide guidelines for future research based on the lessons learned.
However, Exposito-Ventura does not teach the specific ordered sequence of limitations of independent claims 1, 8, 15, nor otherwise cure the deficiencies of Katariya and Szeto.
Moreover, since the specific ordered combined sequence of claim elements recited in claims 1, 8, 15, cannot be found in the cited prior art and can only be found as recited in Applicant’s Specification, any combination of the cited references and/or additional references(s) to teach all the claim elements, including the features discussed above, would be the result of impermissible hindsight reconstruction. Accordingly, any combination with Katariya, Szeto, Exposito-Ventura, and/or any other additional reference(s) would be improper to teach the claimed invention.
The prior art rejection is hereby withdrawn.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 15, 16, 17, 20 are rejected under is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant(s) regard as their invention.
Claim 15 recites “…the allocated storage…”, it is unclear to what this element refers. Further, this element lacks antecedent basis. Appropriate correction is required.
Claims 16, 17, 20 depend on claim 15 and do not cure the aforementioned deficiencies of claim 15, and thus, claims 16, 17, 20 are rejected for the reasons set forth above regarding claim 15 as a result.
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 1-4, 7-11, 14-17, and 20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 recite, “A method comprising:
determining portions of a … to provide a plurality of test messages, customizing the portions of the … to create the plurality of test messages by producing varied portions of the … corresponding to each of the test messages;
publishing the varied portions of the … for a specified period, wherein each of the varied portions of the … corresponds to a …, allocating storage for computations with respect to a baseline message treatment,
assaying, over time, using a response module and the …, a metric corresponding to a message response from an independent group of recipients for each of the test messages;
deriving, iteratively over time, using a difference module and the storage, a comparative difference between an assayed value of the metric for the message response and a baseline value of the metric corresponding to the baseline message treatment;
estimating, iteratively over time, using a variance module, a variance of an average of the metric for the plurality of test messages;
calculating a current confidence value using a confidence module, iteratively over time to produce a confidence sequence in the allocated storage, the current confidence value based on the variance and an error-corrected p-value normalized within confidence bounds, wherein the current confidence value corresponds to a current difference value for the comparative difference; and
dynamically displaying, while updating over time and using an interface module, the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a …, wherein the … while simultaneously updating the confidence sequence.”
Claim 8 recite, “… to perform operations of
determining portions of a … to provide a plurality of test messages, customizing the portions of the … to create the plurality of test messages by producing varied portions of the … corresponding to each of the test messages, wherein each of the varied portions of the … corresponds to a …, allocating storage for computations with respect to a baseline message treatment, and dynamically displaying a confidence sequence:
a … configured to publish the varied portions of the … for a specified period, a response module configured to assay, over time, using the …, a metric corresponding to a message response from an independent group of recipients for each of the test messages;
a … configured to derive, iteratively over time, using the storage, a comparative lift for an assayed value of the metric for the message response relative a baseline value of the metric corresponding to the baseline message treatment;
a … configured to estimate, iteratively over time, a variance of an average of the metric for the plurality of test messages;
a … configured to calculate, iteratively over time to produce a confidence sequence in the allocated storage, a current confidence value based on the variance and an error-corrected p-value, the current confidence value to produce the confidence sequence; and
an … to, while updating over time, dynamically display the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a … while simultaneously updating the confidence sequence.”
Claim 15 recite, “… to perform operations comprising:
determining portions of a … to provide a plurality of test messages;
customizing the portions of the … to create the plurality of test messages by producing varied portions of the … corresponding to each of the test messages;
publishing the varied portions of the … for a specified period, wherein each of the varied portions of the … corresponds to a …;
assaying, over time, using the …, a metric corresponding to a message response from an independent group of recipients for each of the test messages;
a step for producing, iteratively over time, a current confidence value to produce a confidence sequence in the allocated storage, the current confidence value corresponding to a current difference value for a comparative difference between an assayed value of the metric for the message response and a baseline value of the metric; and
dynamically displaying, while updating over time and using an interface module, the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a …, wherein the … while simultaneously updating the confidence sequence.“
Analyzing under Step 2A, Prong 1:
The limitations regarding, …determining portions of a … to provide a plurality of test messages, customizing the portions of the … to create the plurality of test messages by producing varied portions of the … corresponding to each of the test messages; publishing the varied portions of the … for a specified period, wherein each of the varied portions of the … corresponds to a …, allocating storage for computations with respect to a baseline message treatment, assaying, over time, using a response module and the …, a metric corresponding to a message response from an independent group of recipients for each of the test messages; deriving, iteratively over time, using a difference module and the storage, a comparative difference between an assayed value of the metric for the message response and a baseline value of the metric corresponding to the baseline message treatment; estimating, iteratively over time, using a variance module, a variance of an average of the metric for the plurality of test messages; calculating a current confidence value using a confidence module, iteratively over time to produce a confidence sequence in the allocated storage, the current confidence value based on the variance and an error-corrected p-value normalized within confidence bounds, wherein the current confidence value corresponds to a current difference value for the comparative difference; and dynamically displaying, while updating over time and using an interface module, the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a …, wherein the … while simultaneously updating the confidence sequence.… determining portions of a … to provide a plurality of test messages, customizing the portions of the … to create the plurality of test messages by producing varied portions of the … corresponding to each of the test messages, wherein each of the varied portions of the … corresponds to a …, allocating storage for computations with respect to a baseline message treatment, and dynamically displaying a confidence sequence: a … configured to publish the varied portions of the … for a specified period, a response module configured to assay, over time, using the …, a metric corresponding to a message response from an independent group of recipients for each of the test messages; a … configured to derive, iteratively over time, using the storage, a comparative lift for an assayed value of the metric for the message response relative a baseline value of the metric corresponding to the baseline message treatment; a … configured to estimate, iteratively over time, a variance of an average of the metric for the plurality of test messages; a … configured to calculate, iteratively over time to produce a confidence sequence in the allocated storage, a current confidence value based on the variance and an error-corrected p-value, the current confidence value to produce the confidence sequence; and an … to, while updating over time, dynamically display the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a … while simultaneously updating the confidence sequence…determining portions of a … to provide a plurality of test messages; customizing the portions of the … to create the plurality of test messages by producing varied portions of the … corresponding to each of the test messages; publishing the varied portions of the … for a specified period, wherein each of the varied portions of the … corresponds to a …; assaying, over time, using the …, a metric corresponding to a message response from an independent group of recipients for each of the test messages; a step for producing, iteratively over time, a current confidence value to produce a confidence sequence in the allocated storage, the current confidence value corresponding to a current difference value for a comparative difference between an assayed value of the metric for the message response and a baseline value of the metric; and dynamically displaying, while updating over time and using an interface module, the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a …, wherein the … while simultaneously updating the confidence sequence…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to, …determining portions of a … to provide a plurality of test messages, customizing the portions of the … to create the plurality of test messages by producing varied portions of the … corresponding to each of the test messages; publishing the varied portions of the … for a specified period, wherein each of the varied portions of the … corresponds to a …, allocating storage for computations with respect to a baseline message treatment, assaying, over time, using a response module and the …, a metric corresponding to a message response from an independent group of recipients for each of the test messages; deriving, iteratively over time, using a difference module and the storage, a comparative difference between an assayed value of the metric for the message response and a baseline value of the metric corresponding to the baseline message treatment; estimating, iteratively over time, using a variance module, a variance of an average of the metric for the plurality of test messages; calculating a current confidence value using a confidence module, iteratively over time to produce a confidence sequence in the allocated storage, the current confidence value based on the variance and an error-corrected p-value normalized within confidence bounds, wherein the current confidence value corresponds to a current difference value for the comparative difference; and dynamically displaying, while updating over time and using an interface module, the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a …, wherein the … while simultaneously updating the confidence sequence.… determining portions of a … to provide a plurality of test messages, customizing the portions of the … to create the plurality of test messages by producing varied portions of the … corresponding to each of the test messages, wherein each of the varied portions of the … corresponds to a …, allocating storage for computations with respect to a baseline message treatment, and dynamically displaying a confidence sequence: a … configured to publish the varied portions of the … for a specified period, a response module configured to assay, over time, using the …, a metric corresponding to a message response from an independent group of recipients for each of the test messages; a … configured to derive, iteratively over time, using the storage, a comparative lift for an assayed value of the metric for the message response relative a baseline value of the metric corresponding to the baseline message treatment; a … configured to estimate, iteratively over time, a variance of an average of the metric for the plurality of test messages; a … configured to calculate, iteratively over time to produce a confidence sequence in the allocated storage, a current confidence value based on the variance and an error-corrected p-value, the current confidence value to produce the confidence sequence; and an … to, while updating over time, dynamically display the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a … while simultaneously updating the confidence sequence…determining portions of a … to provide a plurality of test messages; customizing the portions of the … to create the plurality of test messages by producing varied portions of the … corresponding to each of the test messages; publishing the varied portions of the … for a specified period, wherein each of the varied portions of the … corresponds to a …; assaying, over time, using the …, a metric corresponding to a message response from an independent group of recipients for each of the test messages; a step for producing, iteratively over time, a current confidence value to produce a confidence sequence in the allocated storage, the current confidence value corresponding to a current difference value for a comparative difference between an assayed value of the metric for the message response and a baseline value of the metric; and dynamically displaying, while updating over time and using an interface module, the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a …, wherein the … while simultaneously updating the confidence sequence…; therefore, the claims are directed to a mental process.
Further, …determining portions of a … to provide a plurality of test messages, customizing the portions of the … to create the plurality of test messages by producing varied portions of the … corresponding to each of the test messages; publishing the varied portions of the … for a specified period, wherein each of the varied portions of the … corresponds to a …, allocating storage for computations with respect to a baseline message treatment, assaying, over time, using a response module and the …, a metric corresponding to a message response from an independent group of recipients for each of the test messages; deriving, iteratively over time, using a difference module and the storage, a comparative difference between an assayed value of the metric for the message response and a baseline value of the metric corresponding to the baseline message treatment; estimating, iteratively over time, using a variance module, a variance of an average of the metric for the plurality of test messages; calculating a current confidence value using a confidence module, iteratively over time to produce a confidence sequence in the allocated storage, the current confidence value based on the variance and an error-corrected p-value normalized within confidence bounds, wherein the current confidence value corresponds to a current difference value for the comparative difference; and dynamically displaying, while updating over time and using an interface module, the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a …, wherein the … while simultaneously updating the confidence sequence.… determining portions of a … to provide a plurality of test messages, customizing the portions of the … to create the plurality of test messages by producing varied portions of the … corresponding to each of the test messages, wherein each of the varied portions of the … corresponds to a …, allocating storage for computations with respect to a baseline message treatment, and dynamically displaying a confidence sequence: a … configured to publish the varied portions of the … for a specified period, a response module configured to assay, over time, using the …, a metric corresponding to a message response from an independent group of recipients for each of the test messages; a … configured to derive, iteratively over time, using the storage, a comparative lift for an assayed value of the metric for the message response relative a baseline value of the metric corresponding to the baseline message treatment; a … configured to estimate, iteratively over time, a variance of an average of the metric for the plurality of test messages; a … configured to calculate, iteratively over time to produce a confidence sequence in the allocated storage, a current confidence value based on the variance and an error-corrected p-value, the current confidence value to produce the confidence sequence; and an … to, while updating over time, dynamically display the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a … while simultaneously updating the confidence sequence…determining portions of a … to provide a plurality of test messages; customizing the portions of the … to create the plurality of test messages by producing varied portions of the … corresponding to each of the test messages; publishing the varied portions of the … for a specified period, wherein each of the varied portions of the … corresponds to a …; assaying, over time, using the …, a metric corresponding to a message response from an independent group of recipients for each of the test messages; a step for producing, iteratively over time, a current confidence value to produce a confidence sequence in the allocated storage, the current confidence value corresponding to a current difference value for a comparative difference between an assayed value of the metric for the message response and a baseline value of the metric; and dynamically displaying, while updating over time and using an interface module, the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a …, wherein the … while simultaneously updating the confidence sequence…, under the broadest reasonable interpretation, are humans testing marketing message effectiveness to other humans, therefore it is, commercial interactions and managing interactions between people. Thus, the claims are directed to certain methods of organizing human activity.
Additionally, …determining portions of a … to provide a plurality of test messages, customizing the portions of the … to create the plurality of test messages by producing varied portions of the … corresponding to each of the test messages; publishing the varied portions of the … for a specified period, wherein each of the varied portions of the … corresponds to a …, allocating storage for computations with respect to a baseline message treatment, assaying, over time, using a response module and the …, a metric corresponding to a message response from an independent group of recipients for each of the test messages; deriving, iteratively over time, using a difference module and the storage, a comparative difference between an assayed value of the metric for the message response and a baseline value of the metric corresponding to the baseline message treatment; estimating, iteratively over time, using a variance module, a variance of an average of the metric for the plurality of test messages; calculating a current confidence value using a confidence module, iteratively over time to produce a confidence sequence in the allocated storage, the current confidence value based on the variance and an error-corrected p-value normalized within confidence bounds, wherein the current confidence value corresponds to a current difference value for the comparative difference; and dynamically displaying, while updating over time and using an interface module, the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a …, wherein the … while simultaneously updating the confidence sequence.… determining portions of a … to provide a plurality of test messages, customizing the portions of the … to create the plurality of test messages by producing varied portions of the … corresponding to each of the test messages, wherein each of the varied portions of the … corresponds to a …, allocating storage for computations with respect to a baseline message treatment, and dynamically displaying a confidence sequence: a … configured to publish the varied portions of the … for a specified period, a response module configured to assay, over time, using the …, a metric corresponding to a message response from an independent group of recipients for each of the test messages; a … configured to derive, iteratively over time, using the storage, a comparative lift for an assayed value of the metric for the message response relative a baseline value of the metric corresponding to the baseline message treatment; a … configured to estimate, iteratively over time, a variance of an average of the metric for the plurality of test messages; a … configured to calculate, iteratively over time to produce a confidence sequence in the allocated storage, a current confidence value based on the variance and an error-corrected p-value, the current confidence value to produce the confidence sequence; and an … to, while updating over time, dynamically display the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a … while simultaneously updating the confidence sequence…determining portions of a … to provide a plurality of test messages; customizing the portions of the … to create the plurality of test messages by producing varied portions of the … corresponding to each of the test messages; publishing the varied portions of the … for a specified period, wherein each of the varied portions of the … corresponds to a …; assaying, over time, using the …, a metric corresponding to a message response from an independent group of recipients for each of the test messages; a step for producing, iteratively over time, a current confidence value to produce a confidence sequence in the allocated storage, the current confidence value corresponding to a current difference value for a comparative difference between an assayed value of the metric for the message response and a baseline value of the metric; and dynamically displaying, while updating over time and using an interface module, the confidence sequence from the allocated storage including the current confidence value for each of the plurality of test messages in visual alignment on a …, wherein the … while simultaneously updating the confidence sequence…, are mathematical concepts.
Accordingly, the claims are directed to a mental process, certain methods of organizing human activity, mathematical concepts, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 8, 15: A system comprising: a memory component; a processing device coupled to the memory component, response module, difference module, variance module, confidence module, A non-transitory computer-readable medium storing executable instructions, which when executed by a processing device, cause the processing device, interface module, messaging server, display device, display device is configured to be scrollable, web site, cookie. server
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “transmitting…”, “…publishing…”, “assaying…”, “…storage for computations…”, “displaying… scrollable”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “transmitting…”, “…publishing…”, “assaying…”, “…storage for computations…”, data output – “displaying… scrollable”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0054] Staying with FIG. 9, area906 lists the pages, rows, and messages displayed for the experiment. In this particular example, there is one page of messaging treatments totaling80 rows. Thus, 80 messages are being tested, including the baseline message. Currently, statistics for messaging treatments one through five are being displayed. Scroll bar908 provides the capability to selectively scroll through results of the test in process while statistics are being updated over time. The analytics application can display these confidence values and other values sequentially over time along with the current difference, or "lift," updating all of these values while maintaining the accuracy of the values and providing a visual display that can be scrolled to provide for examination of the values for all treatments as they are updated while the experiment proceeds.
[0055] FIG. 10 is a diagram of an example of a computing system that can provide anytime-valid confidence sequences when testing multiple messaging treatments according to certain embodiments. System1000 includes a processing device1002 communicatively coupled to one or more memory devices. The processing device1002 executes computer-executable program code stored in the memory component1004. Examples of the processing device1002 include a microprocessor, an application-specific integrated circuit ("ASIC"), a field- programmable gate array ("FPGA"), or any other suitable processing device. The processing device1002 can include any number of processing devices, including a single processing device. The memory component1004 includes any suitable non-transitory computer-readable medium for storing data, program code instructions, or both. A computer-readable medium can include any electronic, optical, magnetic, or other storage device capable of providing a processor with computer-readable, executable instructions or other program code. The memory component can include multiple memory devices to provide a computer-readable medium. Non-limiting examples of a computer-readable medium include a magnetic disk, a memory chip, a ROM, a RAM, an ASIC, optical storage, magnetic tape or other magnetic storage, or any other medium from which a processing device can read instructions. The instructions may include processor- specific instructions generated by a compiler or an interpreter from code written in any suitable computer-programming language, including, for example, C, C++, C#, Visual Basic, Java, Python, Perl, and JavaScript.
[0058] Staying with FIG. 10, in some embodiments, the computing system1000 also includes the presentation device1015. A presentation device1015 can include any device or group of devices suitable for providing visual, auditory, or other suitable sensory output. In examples, presentation device1015 provides the dynamic display of anytime-valid confidence sequences for multiple treatments. Non-limiting examples of the presentation device1015 include a touchscreen, a monitor, a separate mobile computing device, etc. In some aspects, the presentation device1015 can include a remote client-computing device that communicates with the computing system1000 using one or more data networks. System1000 may be implemented as a unitary computing device, for example, a notebook or mobile computer. Alternatively, as an example, the various devices included in system1000 may be distributed and interconnected by interfaces or a network with a central or main computing device including one or more processors.
[0059] Numerous specific details are set forth herein to provide a thorough understanding of the claimed subject matter. However, those skilled in the art will understand that the claimed subject matter may be practiced without these specific details. In other instances, methods, apparatuses, or systems that would be known by one of ordinary skill have not been described in detail so as not to obscure claimed subject matter.
[0060] Unless specifically stated otherwise, it is appreciated that throughout this specification discussions utilizing terms such as "generating,""assaying,""processing,""computing,""determining," and "identifying" or the like refer to actions or processes of a computing device, such as one or more computers or a similar electronic computing device or devices that manipulate or transform data represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the computing platform.
[0061] The system or systems discussed herein are not limited to any particular hardware architecture or configuration. A computing device can include any suitable arrangement of components that provide a result conditioned on one or more inputs. Suitable computing devices include multi-purpose microprocessor-based computer systems accessing stored software that programs or configures the computing system from a general-purpose computing apparatus to a specialized computing apparatus implementing one or more implementations of the present subject matter. Any suitable programming, scripting, or other type of language or combinations of languages may be used to implement the teachings contained herein in software to be used in programming or configuring a computing device
[0064] Where devices, systems, components or modules are described as being configured to perform certain operations or functions, such configuration can be accomplished, for example, by designing electronic circuits to perform the operation, by programming programmable electronic circuits (such as microprocessors) to perform the operation such as by executing computer instructions or code, or processors or cores programmed to execute code or instructions stored on a non-transitory memory medium, or any combination thereof. Processes can communicate using a variety of techniques including but not limited to conventional techniques for inter-process communications, and different pairs of processes may use different techniques, or the same pair of processes may use different techniques at different times. Headings, lists, and numbering included herein are for ease of explanation only and are not meant to be limiting.
[0065] While the present subject matter has been described in detail with respect to specific embodiments thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing, may readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, it should be understood that the present disclosure has been presented for purposes of example rather than limitation and does not preclude inclusion of such modifications, variations, and/or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-4, 7-11, 14-17, and 20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PO HAN MAX LEE whose telephone number is (571) 272-3821. The examiner can normally be reached on Mon-Thurs 8:00 am - 7:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PO HAN LEE/Primary Examiner, Art Unit 3623