DETAILED ACTION
Status of Claims
• The following is an office action in response to the communication filed 07/07/2026.
• Claims 1-7, 10-14, and 16-19 have been amended.
• Claims 1-20 are currently pending and have been examined.
Priority
The examiner acknowledges that the instant application is a continuation of US Patent No. 12059625, filed 12/08/2021.
Information Disclosure Statement
Information Disclosure Statement received 05/13/2026 has been reviewed and considered.
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 a judicial exception without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
First, it is determined whether the claims are directed to a statutory category of invention. See MPEP 2106.03(II). In the instant case, claims 1-9 are directed to a process, and claims 10-20 are directed to a machine. Therefore, claims 1-20 are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine if the claims are directed to a judicial exception. See MPEP 2106.04. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong 1 of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong 2 of Step 2A). See MPEP 2106.04.
Taking claim 1 as representative, claim 1 recites at least the following limitations that are believed to recite an abstract idea:
determining, based at least on analyzing data corresponding to one or more sessions and indicative of user inputs, a distribution of values of an input metric indicating at least one of one or more rates or one or more frequencies of the user inputs over time, the distribution corresponding to a plurality of users;
selecting at least one of the one or more as one or more recommendations for one or more users based at least on comparing one or more values of the input metric that correspond to the one or more users to the distribution values of the input metric; and
sending data that causes presentation of the one or more recommendations associated with the one or more users.
Further, claim 16 recites similar limitations as claim 1 including at least the following limitations that are believed to recite an abstract idea:
present one or more recommendations of associated with one or more users, the one or more recommendations determined based at least on comparing one or more values of an input metric that correspond to the one or more users to a distribution of values of the input metric, the values indicating at least one of one or more rates or one or more frequencies of user inputs, the user inputs provided.
The above limitations recite the concept of providing recommendations based on user behavior. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, providing product recommendations is a sales and marketing activity. This is further illustrated in paragraph [0002] of the Specification, describing the invention relates to retailers providing recommendations. Further, these limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Specifically, the receiving of data and analysis are observations, evaluations, and judgements. Independent claim 10 recites similar limitations as claim 1 and as such, claim 10 falls within the same identified grouping of abstract ideas. Accordingly, under Prong One of Step 2A of the Alice/Mayo test, claims 1, 10, and 16 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the MPEP, claims 1, 10, and 16 recite additional elements, such as application sessions; one or more tactile input devices; one or more applications; transmitting data; one or more devices; a system comprising: one or more processors to perform operations; at least one processor comprising: one or more circuits; and one or more applications running in a cloud-hosted computing environment. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Although these additional computer-related elements are recited, claims 1, 10, and 16 merely invoke such additional elements as a tool to perform the abstract idea. Implementing an abstract idea on a generic computer is not indicative of integration into a practical application. Similar to the limitations of Alice, claims 1, 10, and 16 merely recite a commonplace business method (i.e., providing recommendations based on user behavior) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 1, 10, and 16 generally link the use of the abstract idea to a particular technological environment or field of use. The courts have identified various examples of limitations as merely indicating a field of use/technological environment in which to apply the abstract idea, such as specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer (see FairWarning v. Iatric Sys.). Likewise, claims 1, 10, and 16 specifying that the abstract idea of providing recommendations based on user behavior is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the MPEP, when considered both individually and as a whole, the limitations of claims 1, 10, and 16 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 1, 10, and 16 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 1, 10, and 16 are “directed to” an abstract idea (Step 2A: YES).
Next, under Step 2B, the claims are analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract idea. See MPEP 2106.05. The instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least the following reasons.
Returning to independent claims 1, 10, and 16, these claims recite additional elements, such as application sessions; one or more tactile input devices; one or more applications; transmitting data; one or more devices; a system comprising: one or more processors to perform operations; at least one processor comprising: one or more circuits; and one or more applications running in a cloud-hosted computing environment. As discussed above with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Moreover, the limitations of claims 1, 10, and 16 are manual processes, e.g., receiving information, analyzing information, etc. The courts have indicated that mere automation of manual processes is not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)). Furthermore, as discussed above with respect to Prong Two of Step 2A, claims 1, 10, and 16 merely recite the additional elements in order to further define the field of use of the abstract idea, therein attempting to generally link the use of the abstract idea to a particular technological environment, such as the Internet or computing networks (see Ultramercial, Inc. v. Hulu, LLC. (Fed. Cir. 2014); Bilski v. Kappos (2010); MPEP 2106.05(h)). Similar to FairWarning v. Iatric Sys., claims 1, 10, and 16 specifying that the abstract idea of providing recommendations based on user behavior is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claim to the computer field, i.e., to execution on a generic computer.
Even when considered as an ordered combination, the additional elements do not add anything that is not already present when they are considered individually. In Alice Corp., the Court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘[a]dd nothing…that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Id. (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, viewed as a whole, claims 1, 10, and 16 simply convey the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in claims 1, 10, and 16 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO).
Dependent claims 2-9, 11-15, and 17-20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they recite an abstract idea, are not integrated into a practical application, and do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-9, 11-15, and 17-20 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they further recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors and managing personal behavior or relationships or interactions between people. These claims additionally fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Dependent claims 2-8, 11-14, and 17-18 fail to identify additional elements and as such, are not indicative of integration into a practical application. Dependent claims 9, 15, and 19-20 further identify additional elements, such as one or more of at least one touch-screen, at least one touch-screen display, at least one mouse, at least one keyboard, at least one controller, or at least one remote; video data; pixel changes; and a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a local computing device; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; a system implemented using an online store; a system using a collaborative content creation platform for multi-dimensional assets; or a system implemented at least partially using cloud computing resources. Similar to discussion above the with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). As such, under Step 2A, dependent claims 2-9, 11-15, and 17-20 are “directed to” an abstract idea. Similar to the discussion above with respect to claims 1, 10, and 16, dependent claims 2-9, 11-15, and 17-20 analyzed individually and as an ordered combination, invoke such additional elements as a tool to perform the abstract idea and merely indicate a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, and therefore, do not amount to significantly more than the abstract idea itself. See MPEP 2106.05(f)(2). Accordingly, under the Alice/Mayo test, claims 1-20 are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-12, 14-16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over previously cited Forte (US 20130138585 A1), hereafter Forte, in view of newly cited Kotecha et al. (US 20150036494 A1), hereinafter Kotecha.
In regards to claim 1, Forte discloses a method comprising (Forte: [abstract]):
determining, based at least on analyzing data corresponding to one or more application sessions and indicative of user inputs to one or more tactile input devices, a distribution of values of an input metric of the user inputs over time, the distribution corresponding to a plurality of users of one or more applications (Forte: [0005] – “a obtaining an input model representing user interaction patterns during execution of a first application”; [0032] – “input model of FIG. 6…may be represented in…a distribution probability”; [0027-0028] and Fig. 3B – “FIG. 3B depicts an exemplary input pattern which includes input states of a long swipe along a right-left axis, followed by a hold, followed by a release. Transition probabilities between input states are represented by probabilities, a and b. Transition times between the input states are represented by values X, Y, and Z…The input pattern of FIG. 3B is suggestive of a gaming application…monitors transitions between input states and derives transition probabilities between input states. Input states (A1, B1, etc.) in FIG. 4 represent types of input states, such as a tap, swipe, button press, tilt, etc. The application begins with input state A1, which may correspond to a tap on an initial menu screen. As the user interacts with the application, the transition probabilities between each input state are recorded. As shown in FIG. 4, user input transitions from input state A1 to input state A2 with a 0.6 probability. In other words, 6 out of 10 times, input state A1 is followed by input state A2”; [0030] – “the transition probabilities for the various input states”; [0012] – “Device 12 includes a number of input devices including a touchscreen 28…Touchscreen 28 may be a capacitive-type touchscreen, such as that used in the iPad.TM. device. Touchscreen 28 allows a variety of user inputs including tap”; [0035-0037] – “the recommendation engine 19 executing on server 14 compares the input model to reference models in database 18…the input model for the application on device 12 is compared to reference models in database 18. The reference models in database 18 have the same format as the input model. The reference models in database 18 may be derived from user interaction patterns obtained from a wide population of users using reference applications…reference applications are other applications that may be recommended to a user of device 12… a similarity in the distribution of transition probabilities”; The examiner notes that consistent with Claim 9, a touch screen has been interpreted to be a tactile input device);
selecting at least one application of the one or more applications as one or more recommendations for one or more users based at least on comparing one or more values of the input metric that correspond to the one or more users to the distribution of the values of the input metric (Forte: [0035-0038] – “the recommendation engine 19 executing on server 14 compares the input model to reference models in database 18…the input model for the application on device 12 is compared to reference models in database 18. The reference models in database 18 have the same format as the input model. The reference models in database 18 may be derived from user interaction patterns obtained from a wide population of users using reference applications, where each reference model corresponds to a reference application. The reference applications are other applications that may be recommended to a user of device 12… determining a similarity between the input state patterns in the main input pattern and a similarity in the distribution of transition probabilities…a similarity in the distribution of transition probabilities is determined. The similarity in the distribution of transition probabilities may be determined by matching the array of transition probabilities for the input model to the array of transition probabilities for the reference model…a similarity in the distribution of transition times is determined”; [0040] – “Based on the similarity of…the transition probabilities and the transition times, the recommendation engine 19 recommends one or more reference applications at 114”); and
transmitting data that causes presentation of the one or more recommendations on one or more devices associated with the one or more users (Forte: [0040] – “Based on the similarity of …the transition probabilities and the transition times, the recommendation engine 19 recommends one or more reference applications at 114. The recommendation engine 19 recommends the application corresponding to the reference model by initiating transmission of a message to the device 12, notifying device 12 of the recommended application. The message may include options for downloading the recommended application to device 12 for installation”).
Forte discloses a distribution of values of an input metric (Forte: [0027-0028]; [0032]), yet Forte does not explicitly disclose the distribution indicating at least one of one or more rates or one or more frequencies.
However, Kotecha teaches a similar user behavior analysis method (Kotecha: [abstract]), including
the distribution indicating at least one of one or more rates or one or more frequencies (Kotecha: [0079] – “types of EPG requests that are being received. If for example, there is an increase in requests for non-game-related content for a particular MBSFN…This data may be analyzed as a frequency distribution”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the frequencies of Kotecha in the method of Forte because Forte already discloses a distribution and Kotecha is merely demonstrating what the distribution may be. Additionally, it would have been obvious to have included the distribution indicating at least one of one or more rates or one or more frequencies as taught by Kotecha because frequencies are well-known and the use of it in a recommendation setting would have improved determinations of items customers are using (Kotecha: [0079]).
In regards to claim 2, Forte/Kotecha teaches the method of claim 1. Forte further discloses wherein the comparing includes evaluating whether the one or more values of the input metric fall within a portion of the distribution that corresponds to a percentage of the plurality of users (Forte: [0031] – “the input modeling application determines a main input pattern based on the transition probabilities…Determining the main input pattern is performed by analyzing the transition probabilities between input states and detecting a group of input states which are most probable. This may be done by adding the transition probabilities or locating transition probabilities that are dominant, i.e., exceed other local transition probabilities. As shown in FIG. 4, the transition probabilities between input states A2-A3-A4-A5 indicate that the bulk of the user interaction with the application occurs within these states, once leaving initial state A1”; [0028] and Fig. 4 – “As shown in FIG. 4, user input transitions from input state A1 to input state A2 with a 0.6 probability. In other words, 6 out of 10 times, input state A1 is followed by input state A2”).
In regards to claim 3, Forte/Kotecha teaches the method of claim 1. Forte further discloses wherein the selecting is based at least on: computing a similarity score based at least on the computing; determining the similarity score; and selecting the at least one application based at least on the similarity score (Forte: [0035-0038] – “the recommendation engine 19 executing on server 14 compares the input model to reference models in database 18…the input model for the application on device 12 is compared to reference models in database 18. The reference models in database 18 have the same format as the input model. The reference models in database 18 may be derived from user interaction patterns obtained from a wide population of users using reference applications, where each reference model corresponds to a reference application. The reference applications are other applications that may be recommended to a user of device 12…determining a similarity between the input state patterns in the main input pattern and a similarity in the distribution of transition probabilities. The degree of similarity in the input state patterns may be determined by comparing sequences of input states in the input model and reference model to detect similar patterns of input states… computing a sum of the differences between the transition probabilities of the two arrays”; [0040] – “Based on the similarity of the input state patterns, the transition probabilities and the transition times, the recommendation engine 19 recommends one or more reference applications at 114”).
Forte discloses a similarity score (Forte: [0035-0038]), yet Forte does not explicitly disclose determining that a score satisfies a threshold; and selecting based at least on the score satisfying the threshold.
However, Kotecha teaches a similar user behavior analysis method (Kotecha: [abstract]), including
determining that a score satisfies a threshold; and selecting based at least on the score satisfying the threshold (Kotecha: [0074] – “A first content item receiving bad rating in a threshold number of survey responses may have its ranking reduced by one step while a second content item receiving a good rating from the threshold number of responses may have its ranking increased by one step”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Kotecha with Forte for the reasons identified above with respect to claim 1.
In regards to claim 4, Forte/Kotecha teaches the method of claim 1. Forte further discloses wherein the selecting is based at least on the one or more values of the input metric relative to the distribution (Forte: [0035-0037] – “the recommendation engine 19 executing on server 14 compares the input model to reference models in database 18…the input model for the application on device 12 is compared to reference models in database 18. The reference models in database 18 have the same format as the input model. The reference models in database 18 may be derived from user interaction patterns obtained from a wide population of users using reference applications, where each reference model corresponds to a reference application. The reference applications are other applications that may be recommended to a user of device 12… The comparison of the input model to a reference model at 112 involves determining a similarity between the input state patterns in the main input pattern and a similarity in the distribution of transition probabilities”; [0040] – “Based on the similarity of the input state patterns, the transition probabilities and the transition times, the recommendation engine 19 recommends one or more reference applications at 114”).
Yet Forte does not explicitly disclose a location of the metric in the distribution relative to a mean of the distribution.
However, Kotecha teaches a similar user behavior analysis method (Kotecha: [abstract]), including
a location of the metric in the distribution relative to a mean of the distribution (Kotecha: [0079] – “This data may be analyzed as a frequency distribution having a mean and a standard deviation. Once a sufficient amount of data has been collected, for example 50 non-accessed items, the EPG server 247 may set…for example to one or two standard deviations of the frequency distribution”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Kotecha with Forte for the reasons identified above with respect to claim 1.
In regards to claim 5, Forte/Kotecha teaches the method of claim 1. Forte further discloses wherein the determining the distribution includes: determining values of instances of an attribute represented by the user inputs; and computing, using the values, one or more statistics corresponding to the attribute, wherein the distribution is based at least on the one or more statistics (Forte: [0038-0039] – “If the input model and reference model share a sufficient similarity in input state patterns, a similarity in the distribution of transition probabilities is determined. The similarity in the distribution of transition probabilities may be determined by matching the array of transition probabilities for the input model to the array of transition probabilities for the reference model. This may be performed by computing a sum of the differences between the transition probabilities of the two arrays… Differences between the transition probabilities in the input model and reference model are determined to indicate similarity. Also, differences between the transition times in the input model and reference model are determined to indicate similarity”; [0027] and Fig. 3B – “an exemplary input pattern which includes input states of a long swipe along a right-left axis, followed by a hold, followed by a release. Transition probabilities between input states are represented by probabilities, a and b. Transition times between the input states are represented by values X, Y, and Z”).
In regards to claim 6, Forte/Kotecha teaches the method of claim 1. Forte further discloses wherein the input metric is indicative of at least one of: a speed corresponding to the user inputs; a velocity corresponding to the user inputs; a response time corresponding to the user inputs; or a pace corresponding to the user inputs (Forte: [0032-0033] – “Each input state is coded to identify the type of input state involved. For example, input states A1 and A5 may be coded to identify tap inputs, input state A3 may represent a swipe along the left-right axis, and input state A4 may represent a swipe along the up-down axis. The input states may further be coded to represent duration of the input state, such as a long swipe, short swipe, etc. The duration may be an average duration across multiple inputs of the same type…a transition between input states includes repeating the same input state or transitioning to another input state. The input model includes bi-directional transitions, such as from A3 to A2 (0.2 probability) and from A2 to A3 (0.5 probability)”; [0028] – “input modeling application 26 captures the transition time (e.g., in seconds or milliseconds) taken for each input state transition”).
In regards to claim 7, Forte/Kotecha teaches the method of claim 1. Forte further discloses wherein the user inputs correspond to at least one of: one or more actuation patterns corresponding to the one or more tactile input devices; one or more touch input patterns corresponding to the one or more tactile input devices; or one or more movement patterns corresponding to the one or more tactile input devices (Forte: [0027] and Fig. 3B – “FIG. 3B depicts an exemplary input pattern which includes input states of a long swipe along a right-left axis, followed by a hold, followed by a release. Transition probabilities between input states are represented by probabilities, a and b. Transition times between the input states are represented by values X, Y, and Z”; [0012] – “Device 12 includes a number of input devices including a touchscreen 28…Touchscreen 28 may be a capacitive-type touchscreen, such as that used in the iPad.TM. device. Touchscreen 28 allows a variety of user inputs including tap”; [0032-0033] – “Each input state is coded to identify the type of input state involved. For example, input states A1 and A5 may be coded to identify tap inputs, input state A3 may represent a swipe along the left-right axis, and input state A4 may represent a swipe along the up-down axis. The input states may further be coded to represent duration of the input state, such as a long swipe, short swipe, etc.”).
In regards to claim 8, Forte/Kotecha teaches the method of claim 1. Forte further discloses wherein the one or more application sessions include one or more game sessions and the user inputs control gameplay of the one or more game sessions (Forte: [0027] and Fig. 3B – “FIG. 3B depicts an exemplary input pattern which includes input states of a long swipe along a right-left axis, followed by a hold, followed by a release. Transition probabilities between input states are represented by probabilities, a and b. Transition times between the input states are represented by values X, Y, and Z…The input pattern of FIG. 3B is suggestive of a gaming application”; see also [0004]).
In regards to claim 9, Forte/Kotecha teaches the method of claim 1. Forte further discloses wherein the one or more tactile input devices include one or more of at least one touch-screen, at least one touch-screen display, at least one mouse, at least one keyboard, at least one controller, or at least one remote (Forte: [0021] – “Device 12 includes a number of input devices including a touchscreen 28, accelerometer 30, buttons 32, microphone 34 and sensors 36. Touchscreen 28 may be a capacitive-type touchscreen, such as that used in the iPad.TM. device. Touchscreen 28 allows a variety of user inputs including tap (e.g., touching the screen in a single location and rapidly ceasing contact), double tap, slide (e.g., touching the screen, moving in an x and/or y direction, then ceasing contact), hold (e.g., touching the screen and maintaining contact for more than a predetermined time), drag (e.g., touching an object an moving that object on the screen), release (e.g., ceasing contact with the screen)”).
In regards to claim 10, claim 10 is directed to a system. Claim 10 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a method. The method of Forte/Kotecha teaches the limitations of claim 1 as noted above. Forte further discloses a system comprising: one or more processors to perform operations including (Forte: [0020]). Claim 10 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claims 11-12 and 14, all the limitations in system claims 11-12 and 14 are closely parallel to the limitations of method claims 2 and 4-5 analyzed above and rejected on the same bases.
In regards to claim 15, Forte/Kotecha teaches the system of claim 10. Forte further discloses wherein the system is comprised in at least one of: a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a local computing device; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; a system implemented using an online store; a system using a collaborative content creation platform for multi-dimensional assets; or a system implemented at least partially using cloud computing resources (Forte: [0020] – “Device 12 may be a stationary device (e.g., a desktop computer) or a mobile device (e.g., mobile phone, tablet, laptop, media player, personal digital assistant). FIG. 2 illustrates an exemplary device 12, which is a mobile device. Device 12 includes a processor 20, which may be a general-purpose microprocessor executing software code stored in a storage medium to perform the functions described herein”; [0025] and Fig. 1 – “Server 14 communicates with device 12 over network 16 to receive user interaction patterns or input models from device 12”; the examiner notes Fig. 1 displays the devices in communication via a cloud).
In regards to claim 16, Forte discloses at least one processor comprising (Forte: [0020]):
one or more circuits to present one or more recommendations of content using one or more devices associated with one or more users (Forte: [0040] – “Based on the similarity of the input state patterns, the transition probabilities and the transition times, the recommendation engine 19 recommends one or more reference applications at 114. The recommendation engine 19 recommends the application corresponding to the reference model by initiating transmission of a message to the device 12, notifying device 12 of the recommended application. The message may include options for downloading the recommended application to device 12 for installation”),
the one or more recommendations determined based at least on comparing one or more values of an input metric that correspond to the one or more users to a distribution of values of the input metric, the values of user inputs to one or more tactile input devices over time (Forte: [0005] – “a obtaining an input model representing user interaction patterns during execution of a first application”; [0005] – “a obtaining an input model representing user interaction patterns during execution of a first application”; [0032] – “input model of FIG. 6…may be represented in…a distribution probability”; [0027-0028] and Fig. 3B – “FIG. 3B depicts an exemplary input pattern which includes input states of a long swipe along a right-left axis, followed by a hold, followed by a release. Transition probabilities between input states are represented by probabilities, a and b. Transition times between the input states are represented by values X, Y, and Z…The input pattern of FIG. 3B is suggestive of a gaming application…monitors transitions between input states and derives transition probabilities between input states. Input states (A1, B1, etc.) in FIG. 4 represent types of input states, such as a tap, swipe, button press, tilt, etc… For example, as shown in FIG. 4, the application begins with input state A1, which may correspond to a tap on an initial menu screen. As the user interacts with the application, the transition probabilities between each input state are recorded. As shown in FIG. 4, user input transitions from input state A1 to input state A2 with a 0.6 probability. In other words, 6 out of 10 times, input state A1 is followed by input state A2”; [0030] – “the transition probabilities for the various input states”; [0012] – “Device 12 includes a number of input devices including a touchscreen 28…Touchscreen 28 may be a capacitive-type touchscreen, such as that used in the iPad.TM. device. Touchscreen 28 allows a variety of user inputs including tap”; [0035-0037] – “the recommendation engine 19 executing on server 14 compares the input model to reference models in database 18…the input model for the application on device 12 is compared to reference models in database 18. The reference models in database 18 have the same format as the input model. The reference models in database 18 may be derived from user interaction patterns obtained from a wide population of users using reference applications…reference applications are other applications that may be recommended to a user of device 12… a similarity in the distribution of transition probabilities”; [0012] – “Device 12 includes a number of input devices including a touchscreen 28…Touchscreen 28 may be a capacitive-type touchscreen, such as that used in the iPad.TM. device. Touchscreen 28 allows a variety of user inputs including tap”; The examiner notes that consistent with Claim 9, a touch screen has been interpreted to be a tactile input device),
the user inputs provided to one or more applications running in a cloud-hosted computing environment (Forte: [0027] and Fig. 3B – “FIG. 3B depicts an exemplary input pattern which includes input states of a long swipe along a right-left axis, followed by a hold, followed by a release…The input pattern of FIG. 3B is suggestive of a gaming application”; [0020] – “Device 12 may be a stationary device (e.g., a desktop computer) or a mobile device (e.g., mobile phone, tablet, laptop, media player, personal digital assistant). FIG. 2 illustrates an exemplary device 12, which is a mobile device. Device 12 includes a processor 20, which may be a general-purpose microprocessor executing software code stored in a storage medium to perform the functions described herein”; [0025] and Fig. 1 – “Server 14 communicates with device 12 over network 16 to receive user interaction patterns or input models from device 12”; see also [0024]; the examiner notes Fig. 1 displays the devices in communication via a cloud).
Forte discloses a distribution of values of an input metric (Forte: [0027-0028]; [0032]), yet Forte does not explicitly disclose the distribution indicating at least one of one or more rates or one or more frequencies.
However, Kotecha teaches a similar user behavior analysis method (Kotecha: [abstract]), including
the distribution indicating at least one of one or more rates or one or more frequencies (Kotecha: [0079] – “types of EPG requests that are being received. If for example, there is an increase in requests for non-game-related content for a particular MBSFN…This data may be analyzed as a frequency distribution”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the frequencies of Kotecha in the method of Forte because Forte already discloses a distribution and Kotecha is merely demonstrating what the distribution may be. Additionally, it would have been obvious to have included the distribution indicating at least one of one or more rates or one or more frequencies as taught by Kotecha because frequencies are well-known and the use of it in a recommendation setting would have improved determinations of items customers are using (Kotecha: [0079]).
In regards to claim 18, all the limitations in apparatus claim 18 are closely parallel to the limitations of method claim 2 analyzed above and rejected on the same bases.
In regards to claim 20, all the limitations in apparatus claim 20 are closely parallel to the limitations of system claim 15 analyzed above and rejected on the same bases.
Claims 13 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Forte, in view of Kotecha, in view of newly cited Agarwal et al. (US 12670082 B1), hereinafter Agarwal.
In regards to claim 13, Forte/Kotecha teaches the system of claim 10. Yet Forte does not explicitly disclose wherein the distribution is represented using at least one of a histogram, a probability distribution function (PDF), or a cumulative distribution function (CDF).
However, Agarwal teaches a similar user behavior analysis method (Agarwal: Col. 40, Ln. 10-15), including
wherein the distribution is represented using at least one of a histogram, a probability distribution function (PDF), or a cumulative distribution function (CDF) (Agarwal: Col. 40, Ln. 5-15 – “displays a histogram 1708 (displayed as a heatmap) that represents a distribution of durations of a corresponding set of spans, which includes the self-time and the total time of the corresponding spans. In an embodiment, any of the group of spans illustrated in FIG. 17 can be selected to show a detailed span duration histogram, e.g., histogram 1812. The histogram tracks the duration behavior of a similar type of span (e.g., associated with the same service and operation) over a number of requests”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the histogram of Agarwal in the method of Forte/Kotecha because Forte/Kotecha already teaches a distribution and Agarwal is merely demonstrating what the distribution may be. Additionally, it would have been obvious to have included wherein the distribution is represented using at least one of a histogram, a probability distribution function (PDF), or a cumulative distribution function (CDF) as taught by Agarwal because histograms are well-known and the use of it in a recommendation setting would have made analysis more efficient (Agarwal: Col. 39, Ln. 44-48).
In regards to claim 17, all the limitations in apparatus claim 17 are closely parallel to the limitations of system claim 13 analyzed above and rejected on the same bases.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Forte, in view of Kotecha, in view of newly cited Li et al. (US 20180249205 A1), hereinafter Li.
In regards to claim 19, Forte/Kotecha teaches the system of claim 10. Forte further discloses wherein the distribution is determined based at least on determining, using an analysis of data associated with the user inputs, that the user inputs are associated with one or more periods (Forte: [0005] – “a obtaining an input model representing user interaction patterns during execution of a first application”; [0032] – “input model of FIG. 6…may be represented in…a distribution probability”; [0027-0028] and Fig. 3B – “FIG. 3B depicts an exemplary input pattern which includes input states of a long swipe along a right-left axis, followed by a hold, followed by a release. Transition probabilities between input states are represented by probabilities, a and b. Transition times between the input states are represented by values X, Y, and Z…The input pattern of FIG. 3B is suggestive of a gaming application…monitors transitions between input states and derives transition probabilities between input states. Input states (A1, B1, etc.) in FIG. 4 represent types of input states, such as a tap, swipe, button press, tilt, etc.”; [0030] – “the transition probabilities for the various input states”; [0012] – “Device 12 includes a number of input devices including a touchscreen 28…Touchscreen 28 may be a capacitive-type touchscreen, such as that used in the iPad.TM. device. Touchscreen 28 allows a variety of user inputs including tap”; [0035-0037] – “the recommendation engine 19 executing on server 14 compares the input model to reference models in database 18…the input model for the application on device 12 is compared to reference models in database 18. The reference models in database 18 have the same format as the input model. The reference models in database 18 may be derived from user interaction patterns obtained from a wide population of users using reference applications…reference applications are other applications that may be recommended to a user of device 12”).
Yet Forte/Kotecha does not explicitly disclose determining, using an analysis of video data, that are associated with one or more periods in which pixel changes have exceed a threshold.
However, Li teaches a recommendation method (Li: [0054]), including
determining, using an analysis of video data, that are associated with one or more periods in which pixel changes have exceed a threshold (Li: [0039] and Fig. 2 – “An input video from the video database 102 is fed into the shot segmentation module 212 which segments the input video into a plurality of video shots each comprising multiple frames…an input video is segmented into shots based on the change in visual pixels between consecutive frames. The pixel-by-pixel and colour histogram difference between consecutive frames are calculated and if the difference exceeds a pre-determined threshold, the frames are separated into two separate shots”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the pixel analysis of Li in the method of Forte/Kotecha because Forte/Kotecha already teaches an analysis and Li is merely demonstrating what the analysis may be. Additionally, it would have been obvious to have included determining, using an analysis of video data, that are associated with one or more periods in which pixel changes have exceed a threshold as taught by Li because pixel analyses are well-known and the use of it in a recommendation setting would have provided more relevant recommendations (Li: [0042]).
Response to Arguments
Applicant’s arguments, filed 07/07/2026, have been fully considered.
35 U.S.C. § 101
Applicant argues that the claims do not recite an abstract idea because the claims “accomplish a technical effect, and should therefore not be considered a mathematical concept, a method of organizing human activity, or a mental process”. Remarks pages 2-4. The examiner disagrees. As shown in the rejection of claims under 35 U.S.C. 101 above, the limitations directed to the abstract idea are directly quoted and concepts within the identified as belonging to the Certain Methods of Organizing Human Activity and Mental Processes groupings of abstract ideas. With respect to the instant claims, application sessions; one or more tactile input devices; one or more applications; transmitting data; one or more devices; a system comprising: one or more processors to perform operations; at least one processor comprising: one or more circuits; and one or more applications running in a cloud-hosted computing environment have been analyzed as additional elements and accordingly are not analyzed under Step 2A, Prong 1. The claims further recite selecting a product and providing notifications. These claims fall into the Methods of Organizing Human Activity grouping, which includes activity that falls within the enumerated sub-grouping of in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, providing product recommendations is a sales and marketing activity. This is further illustrated in paragraph [0002] of the Specification, describing the invention relates to retailers providing recommendations. Further, these limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. It is noted that the determining is an evaluation and observation, and the selecting and causing presentation are judgements. These claims recite the mental process of collecting information, analyzing it, and displaying certain results of the collection and analysis. Accordingly, these claims recite an abstract idea.
Applicant argues the claims do not recite an abstract idea and are integrated into a practical application because “claimed features provide a technical solution to the problem of personalizing application recommendations by improving application recommendations…the claimed techniques for improving game recommendations based on user playstyle data that accounts for differences in input metrics represent ‘a specific improvement over prior systems,’ not an abstract idea” (Remarks pages 3-6). The examiner disagrees. The MPEP provides guidance on how to evaluate whether claims recite an improvement in the functioning of a computer or an improvement to other technology or technical field. For example, the MPEP states “the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.” The MPEP further states that “[t]he specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art,” and that, “conversely, if the specification explicitly sets forth an improvement but in a conclusory manner…the examiner should not determine the claim improves technology” (see MPEP 2106.04). That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. Looking to the specification is a standard that the courts have employed when analyzing claims as it relates to improvements in technology. For example, in Enfish, the specification provided teaching that the claimed invention achieves benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. Enfish LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016). Additionally, in Core Wireless the specification noted deficiencies in prior art interfaces relating to efficient functioning of the computer. Core Wireless Licensing v. LG Elecs. Inc., 880 F.3d 1356 (Fed Cir. 2018). With respect to McRO, the claimed improvement, as confirmed by the originally filed specification, was “…allowing computers to produce ‘accurate and realistic lip synchronization and facial expressions in animated characters…’” and it was “…the incorporation of the claimed rules, not the use of the computer, that “improved [the] existing technological process” by allowing the automation of further tasks”. McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, (Fed. Cir. 2016).
While the examiner acknowledges that improvements to the functioning of a computer or to any other technology or technical field may constitute integration into a practical application (see MPEP 2106.05(a)), the instant claims do not provide a technical improvement. Rather, the claims provide an improvement to the abstract idea of providing recommendations based on user behavior. It is noted that improving personalization of recommendation is merely an improvement to the abstract idea and not a technical improvement.
Although the claims include computer technology such as application sessions; one or more tactile input devices; one or more applications; transmitting data; one or more devices; a system comprising: one or more processors to perform operations; at least one processor comprising: one or more circuits; and one or more applications running in a cloud-hosted computing environment, such elements are merely peripherally incorporated in order to implement the abstract idea. Put another way, these additional elements are merely used to apply the abstract idea of providing recommendations based on user behavior in a technological environment without effectuating any improvement or change to the functioning of the additional elements or other technology. This is unlike the improvements recognized by the courts in cases such as Enfish, Core Wireless, and McRO. Unlike precedential cases, neither the specification nor the claims of the instant invention identify such a specific improvement to computer capabilities. The instant claims are not directed to technological improvements but are directed to improving the business method of providing recommendation. The claimed process, while arguably resulting in a better process for recommendations, is not providing any improvement to another technology or technical field as the claimed process is not, for example, improving the server and/or computer components that operate the system. Rather, the claimed process is utilizing data sets related to users while still employing the same server and/or computer components used in conventional systems to improve providing recommendations based on user behavior, e.g. a business method, and therefore is merely applying the abstract idea using generic computing components. As such, the claims are not integrated into practical application.
35 U.S.C. § 102/103
Applicant argues the claims are allowable because the cited art does not disclose “‘a distribution of values of an input metric indicating at least one of one or more rates or one or more frequencies of the user inputs over time’” (Remarks pages 6-7). The examiner disagrees. Initially, the examiner notes that the amendments have necessitated a new grounds of rejection and a new reference has been cited to teach the distribution indicating at least one of one or more rates or one or more frequencies. Further, Forte is cited as disclosing a distribution of values of an input metric of the user inputs over time. Forte discloses this in [0027-0028] and [0032], disclosing that an input model may be represented by a distribution probability, where input states include a long swipe along a right-left axis, followed by a hold, followed by a release. Transition probabilities between input states are represented by probabilities, a and b. Transition times between the input states are represented by values X, Y, and Z. Transitions between input states may be monitored to derive transition probabilities between input states. the application begins with input state A1, which may correspond to a tap on an initial menu screen. As the user interacts with the application, the transition probabilities between each input state are recorded. As shown in FIG. 4, user input transitions from input state A1 to input state A2 with a 0.6 probability. In other words, 6 out of 10 times, input state A1 is followed by input state A2. Thus, the transition probability is a distribution of values of an input metric over time. Forte further discloses in [0035-0037] that the reference models are derived from user interaction patterns obtained from a wide population of users using reference applications. Thus, the cited art teaches this limitation in this claim.
Applicant argues the claims are allowable because the cited art does not disclose “selecting at least one application of the one or more applications as one or more recommendations for one or more users based at least on comparing one or more values of the input metric that correspond to the one or more users to the distribution of the values of the input metric” (Remarks page 7). The examiner disagrees. Forte discloses this limitation in this claim in [0035-0038], disclosing comparing the input model to reference models where the reference models may be derived from user interaction patterns obtained from a wide population of users using reference applications. A similarity is determined between the distribution of transition probabilities. The similarity in the distribution of transition probabilities may be determined by matching the array of transition probabilities for the input model to the array of transition probabilities for the reference model. Further, a similarity in the distribution of transition times is determined. Forte further discloses in [0040] that a recommendation is provided based on the similarities. Thus, the cited art discloses this limitation in the claim.
Applicant argues claims 10 and 16 and the dependent claims are allowable for the same reasons as claim 1 (Remarks page 7). The examiner disagrees. The rejection of claim 1 has been maintained for the reasons discussed in the 103 rejection and response to remarks paragraphs above, and the rejections of claims 10, 16, and the dependent claims are maintained for the same, and additional, reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference U, initially cited in the Office action dated 04/07/2026, teaches a method of providing game recommendations to a user based on in-game playing. A user may be profiled during playtime of a game. In game activities may be monitored and suggestions provided to players.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNA MAE MITROS whose telephone number is (571)272-3969. The examiner can normally be reached Monday-Friday from 9:30-6.
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/ANNA MAE MITROS/Examiner, Art Unit 3689
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