DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1, 2, 11, 12, 15 and 20 have been amended.
Claims 1 – 20 are pending.
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.
This subject matter eligibility analysis follows the latest guidance for Patent Subject Matter Eligibility Guidance.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1:
Claims 1-10 are drawn to a method.
Claims 11 - 19 are drawn to a system.
Claim 20 is drawn to a non-transitory CRM.
Thus, initially, under Step 1 of the analysis, it is noted that the claims are directed towards eligible categories of subject matter.
Step 2A:
Prong 1: Does the Claim recite an Abstract idea, Law of Nature, or Natural Phenomenon?
Claims 11-19 are exemplary because they require substantially the same operative limitations of the remaining claims (reproduced below.) Examiner has underlined the claim limitations which recite the abstract idea, discussed in detail in the paragraphs that follow.
11. (Currently Amended) A system of identifying peers for activity recommendations, the system comprising:
memory that stores historic behavior information regarding a user in memory, the information comprising data regarding interaction by a plurality of different users comprising the user with one or more content titles;
a processor that executes instructions stored in memory, wherein execution of the instructions by the processor:
predict, using a computer system comprising at least one processor and a recommendation module, executing adaptive machine learning instructions comprising a neural network trained on user behavior data, an interest in an identified activity and a predicted time period during which the user of the plurality of different users is available to engage in the identified activity by applying machine learning to a set of the historic behavior information associated with the user or with one or more other users that are similarly-situated;
identifies, using the recommendation module, at least one peer from among other users as likely to be interested in the identified activity based on applying the machine learning to a second set of the historic behavior information associated with the at least one peer, the identifying comprising determining that the at least one peer is available is available to engage with the identified activity during the predicted time period and determining that the at least one peer has historic engagement with the identified activity;
generates a display for a user device of the user, wherein generating the display comprises controlling a presentation of a recommendation regarding engagement in the identified activity with the at least one peer during the predicted time period, the display further including at least one other different activity recommendation regarding a different activity associated with a different peer for the predicted time period;
receives, during the same user session, an explicit selection from the user of one of the different activity recommendations presented in the display;
continues, using the recommendation module, to update the stored historic behavior information over; and
updates the controlling of the presentation of the recommendation within the display based on application of adaptive machine learning to the updated historic behavior information wherein updating is further based on tracked user selection from the different recommendations presented in the display, such that subsequent recommendations and peer identifications are dynamically adapted within the same session in response to the user selection.
The claims recite italicized limitations that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG, namely, Mental Processes
More specifically, under this grouping, the italicized limitations represent concepts performed in the human mind (including an observation, evaluation, judgment, opinion). The claims are directed towards the making of inferences based on historical user behavior and generating a display of recommendations regarding recommended activities for a user to engage in with other users during a determined time period.
Prong 2: Does the Claim recite additional elements that integrate the exception in to a practical application of the exception?
Although the claims recite additional limitations, these limitations do not integrate the exception into a practical application of the exception. For example, the claims require additional limitations as follow, (emphasis added): memory for storing data, processors for executing instructions, recommendation modules, applying machine learning to data, and a display to generate a display.
These additional limitations do not represent an improvement to the functioning of a computer, or to any other technology or technical field, (MPEP 2106.05(a)). Nor do they apply the exception using a particular machine, (MPEP 2106.05(b)). Furthermore, they do not effect a transformation. (MPEP 2106.05(c)). Rather, these additional limitations amount to an instruction to “apply” the judicial exception using a computer as a tool to perform the abstract idea. Therefore, since the additional limitations, individually or in combination, are indistinguishable from a computer used as a tool to perform the abstract idea, the analysis continues to Step 2B, below.
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they amount to conventional and routine computer implementation and mere instructions for implementing the abstract idea on generic computing devices.
For example, as pointed out above, the claimed invention recites additional elements facilitating implementation of the abstract idea. Applicant has claimed memory for storing data, processors for executing instructions, recommendation modules, applying machine learning to data, and a display to generate a display.
However, all of these elements viewed individually and as a whole, are indistinguishable from conventional computing elements known in the art. Therefore, the additional elements fail to supply additional elements that yield significantly more than the underlying abstract idea.
As the Alice court cautioned, citing Flook, patent eligibility cannot depend simply on the draftsman’s art. Here, amending the claims with generic computing elements does not (in this Examiner’s opinion), confer eligibility.
Regarding the Berkheimer decision, Applicant’s own specification establishes that these additional elements are generic:
[0014]Aspects of the technology involve determinations of activity recommendations and/or peer recommendations made in response to a user’s “free-to-play” indication. As used herein, a free-to-play indication can include any signal used to make inferences regarding free periods of time in which the user may be interested in participation in a suggested activity. Free-to-play indications can include explicit user inputs to indicate a start time, a stop time, and/or a time duration in which he/she is free to participate in a suggested activity. In other aspects, free-to-play indications can be signals inferred from historic user behaviors, and/or behaviors of other similarly situated users. For example, free-to-play indications may be outputs resulting from a machine-learning algorithm, such as a neural network, configured to make inferences regarding a likelihood of player availability.
[0017] It is understood that the system architecture of environment 100 is intended to conceptually illustrate various functional components used to provide activity and/or peer recommendations. However, a greater or fewer number of hardware and/or software components can be implemented. For example, recommendation system 104 could include multiple computing devices (e.g., servers), as part of a network (e.g., an online gaming network), or as part of a distributed computing system. Additionally, users/players 108, 110, and 112, are intended to help illustrate aspects of the technology that relate to a multi-user platform or gaming environment; a greater number of players may be included, without departing from the scope of the technology.
[0018] Computing devices 108A, 110A, and 112A, can include any of a variety of processor-based system types, including but not limited to one or more of: gaming console/s, smartphone/s, tablet computing device/s, personal computer/s, and/or personal desktop assistant/s (PDAs), or the like. Additionally, as discussed in further detail below, activity recommendation module 104C, and peer recommendation module 104D can be implemented as separate software routines and/or hardware systems, e.g., for providing different recommendations. Alternatively, activity recommendation module 104C and peer recommendation module 104D can be implemented as part of the same software system, e.g., instantiated on similar virtual machines, or as portions of the same machine-learning platform.
Therefore, these elements fail to supply additional elements that yield significantly more than the underlying abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Moreover, the claims do not recite improvements to another technology or technical field. Nor, do the claims improve the functioning of the underlying computer itself -- they merely recite generic computing elements. Furthermore, they do not effect a transformation of a particular article to a different state or thing: the underlying computing elements remain the same.
Concerning preemption, the Federal Circuit has said in Ariosa Diagnostics, Inc., V. Sequenom, Inc., (Fed Cir. June 12, 2015):
The Supreme Court has made clear that the principle of preemption is the basis for the judicial exceptions to patentability. Alice, 134 S. Ct at 2354 (“We have described the concern that drives this exclusionary principal as one of pre-emption”). For this reason, questions on preemption are inherent in and resolved by the § 101 analysis. The concern is that “patent law not inhibit further discovery by improperly tying up the future use of these building blocks of human ingenuity.” Id. (internal quotations omitted). In other words, patent claims should not prevent the use of the basic building blocks of technology—abstract ideas, naturally occurring phenomena, and natural laws. While preemption may signal patent ineligible subject matter, the absence of complete preemption does not demonstrate patent eligibility. In this case, Sequenom’s attempt to limit the breadth of the claims by showing alternative uses of cffDNA outside of the scope of the claims does not change the conclusion that the claims are directed to patent ineligible subject matter. Where a patent’s claims are deemed only to disclose patent ineligible subject matter under the Mayo framework, as they are in this case, preemption concerns are fully addressed and made moot. (Emphasis added.)
For these reasons, it appears that the claims are not patent-eligible under 35 USC §101.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morgenstern et al (US 20120278262) in view of Ground Jr, et al (US 2018/0077090) in view of Reese et al (US 2014/0365313) in view of Levy et al (US 10,275,818)
As per claim 1, Morgenstern discloses:
storing using a memory and a processor of a user device historic behavior information regarding a user in memory, the information comprising data regarding interaction by a plurality of different users comprising the user with one or more content titles; (Morgenstern discloses the determination and storage of user behavior information regarding historic interactions between users and/or a plurality of different users with regard to selected applications) (Morgenstern 0030, 0031, 0045, 0046)
predicting using a computer system that comprises at least on processor and a recommendation module,… an interest in an identified activity and …by applying machine learning to a set of the historic information associated with the user or with one or more other users that are similarly-situated, (Morgenstern discloses the determination of a probability (i.e. predicted interest) of users that are similarly situated by machine learning applied to historical interactions) (Morgenstern 0022, 0023, 0031, 0032, 0033, 0045, 0046)
identifying , using the recommendation module at least one peer from among the other users as likely to be interested in the identified activity based on applying machine learning to a second set of the historic behavior information associated with the at least one peer… and determining that the at least one peer has historic engagement with the identified activity; (Morgenstern discloses the identification of peers or users that are likely interested in an engaging in an activity with a user (Morgenstern 0032). Morgenstern discloses the using machine learning to make a determination of a probability score for candidate users that based on the users historic engagement or use of a particular application (Morgenstern 0034). Morgenstern further discloses the identifying and suggestion of candidate users to the user based upon users with higher predicted likelihood of available to interact with the application and the user) (Morgenstern 0036)
generating in real time a display for a user device of the user, wherein generating the display comprises a presentation of a recommendation regarding engagement in the identified activity with the at least one peer…; (Morgenstern discloses the displaying of a plurality of recommended friends or users for a first user to interact with utilizing a designated application) (Morgenstern 0033, 0041)
continuing, using the recommendation module, to update the stored historic behavior information over time; and updating, in real time…the controlling of the presentation of the recommendation within the display based on application of adaptive machine learning to the updated historic behavior information. (Morgenstern discloses the continued updating of the datastore comprising historical interactions between users or historical behaviors over time (Morgenstern 0033) and the updating of recommendations in response to the highest ranked candidate users at the moment) (Morgenstern 0036)
Morgenstern fails to disclose:
“…Executing adaptive machine learning instructions comprising a neural network trained on user behavior…”
“…a predicted time period during which the user of the plurality of different users is available to engage in the identified activity”
“…wherein the identifying comprising determining that the at least one peer is available to engage with the identified activity during the predicted time period;”
“…during the predicted time period, the display further including at least one other different activity recommendation regarding a different activity associated with a different peer for the predicted time period;”
“receiving, during the same user session, an explicit selection from the user of the different activity recommendation presented in the display;
“…and during the same user session,… wherein updating is further based on tracked user selection from the different activity recommendations presented in the display, such that subsequent recommendations and peer identifications are dynamically adapted within the same session in response to the user selection.
However, in a similar field of activity, Ground discloses a system that utilizes machine leaning to analyze a user’s historical usage of a particular application, such as a messaging application, to determine a predicted availability of the user to interact with another user utilizing the same messaging application (Ground 0024, 0026, 0028). Based upon the determined predicted availability, the system will display to a user notifications upon a display that are based upon the most likely times or a predicted time period a user will be available to interact with in the future using a particular application (Ground 0029, Fig 3B, 3C)
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Morgenstern in view of Ground to use a known technique to improve similar devices in the same way by processing historical behavior such as application usage to determine predicted availability of another user for a future time period. This would be beneficial as it will enable a first user to determine a second user’s possible availabilities without the second user explicitly setting their availabilities in advance.
In a similar field of endeavor, Reese et al teaches the use of a recommendation engine that recommends to a user a plurality of group activities and also recommends multiple different recommended individuals for the plurality of different activities to aid in the scheduling of activities for a user (Reese 0070).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Morgenstern in view of Ground and Reese to provide a system that presents to a user options or alternatives for recommended group activities along with different participants for each recommended group activity for a predicted time period. This would be beneficial as it would the multiple options or activity and/or recommended participant would aid a user to determine which activity and participant they would most like to accept and engage in a future group activity or event.
However, in a similar field of activity, Levy discloses a recommendation system that utilizes machine learning neural networks based upon user actions (i.e. behavior) (Levy 1:40 – 52) wherein a user interface is dynamically updated in real-time based upon user selections wherein the list of recommended items (i.e. subsequent recommendations) are modified or dynamically adapted based upon the system predicting a user’s interest in predicted items (Levy Claim 1).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Morgenstern in view of Levy to use a known technique to improve similar devices in the same way by utilizing a display technique that dynamically modifies a user interface based upon an analyzation of current user selections. This would be beneficial as it would provide a user interface that dynamically adjusts displayed recommendations in real-time to a better cater to the predicted needs or interests of the user.
As per claim 2, further comprising identifying the similarly-situated users, wherein the historical behavior information of the similarly-situated users are used to make a prediction that the user has the predicted interest in the identified activity. (Morgenstern disclose the identification of users based upon prior user interactions (i.e. historical behavior)) (Morgenstern 0033)
As per claim 3, wherein identifying the at least one peer is further based on a level of progress of the at least one peer within the one or more content titles. (Morgenstern discloses the determination of similarities based upon users joining the same group around the same time (i.e. level of progress)) , using the word “poker” and the number of times the user has accepted previous invitations (i.e. level of progress)) (Morgenstern 0034)
As per claim 4, further comprising determining an affinity between the user and the at least one peer based on the level of progress of the at least one peer being similar to a current level of progress of the user. (Morgenstern discloses the determination of similarities based upon users joining the same group around the same time (i.e. level of progress)) , using the word “poker” and the number of times the user has accepted previous invitations (i.e. level of progress)) (Morgenstern 0034)
As per claim 5, further comprising predicting a time period having a duration when the user is available to engage with the content titles based on a prediction regarding the user. ((Combination of Morgenstern ,Ground, Reese and Levy, wherein Morgenstern discloses the prediction and suggestion of a time period having a duration that the users are predicted to be able to engage in application activities with one another) (Morgenstern 0023) (Ground 0029, Fig 3B, 3C)
As per claim 6, wherein the recommendation further includes the predicted time period for the identified activity with the at least one peer. (Combination of Morgenstern ,Ground, Reese and Levy, wherein Morgenstern discloses the prediction and suggestion of a time period having a duration that the users are predicted to be able to engage in application activities with one another) (Morgenstern 0023) (Ground 0029, Fig 3B, 3C)
As per claim 7, wherein identifying the at least one peer is further based on an affinity between the at least one peer and the user in relation to the content titles and overlapping availability at the predicted time period. (Morgenstern 0023)
As per claim 8, wherein identifying the at least one peer is further based on social data associated with one or more social media accounts of the user, wherein the affinity between the at least one peer and the user is based on the social data. (Morgenstern discloses social data that is based in affinity of users in a social network) (Morgenstern 0022)
As per claim 9, further comprising generating a plurality of different recommendations, wherein updating the one or more of the recommendation within the display is further based on tracked user selection from the plurality of different recommendations. (Morgenstern ,Ground, Reese and Levy, as applied above wherein Morgenstern discloses the displaying of a plurality of recommended friends or users for a first user to interact with utilizing a designated application) (Morgenstern 0033, 0041) and Reese teaches the generation of a plurality of different recommendations for recommended activities and different recommended friends based upon historical engagement with the activity) (Reese 0070, 0074)
As per claim 10, wherein updating the stored historic behavior information is further based on the tracked user selection from the plurality of different recommendations. (Morgenstern discloses the historical behavior information is based upon data points comprising tracked user selection of different recommendations such as the acceptance of past invites the user has accepted) (Morgenstern 0034)
Independent claim(s) 11 and 20 is/are rendered obvious by Morgenstern ,Ground, Reese and Levy, based on the same analysis set forth for claim(s) 1, which are similar in claim scope.
Regarding the specific hardware limitation of claim 11, wherein “memory that stores historic behavior information regarding a user in memory, the information comprising data regarding interaction by a plurality of different users comprising the user with one or more content titles; (Morgenstern discloses the use of data stores such as memory to store user profile data and historical game data indicative of every game that the user has played previously) (Morgenstern 0030, 0031)
a processor that executes instructions stored in memory, wherein execution of the instructions by the processor: (Morgenstern discloses the system being implemented by means of processor in cooperation with storage mediums such as memory) (Morgenstern 0045, 0046)
Dependent claim(s) 12 - 19 is/are rendered obvious by Morgenstern ,Ground, Reese and Levy, based on the same analysis set forth for claim(s) 2 – 9 respectively, which are similar in claim scope.
Response to Arguments
Applicant’s arguments with respect to claim(s) claims 1 -20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please see above rejection addressing the newly amended claim language in light of Levy et al.
Regarding the claims being rejection under 35 U.S.C. 101, the Applicant argues in multiple times the following:
The claimed system does not simply automate a conventional business practice or data analysis, but recites a specific, technical solution to recognized problems in the art, including high latency, low responsiveness, and inefficiency in conventional recommendation engines. (Remarks page 10, par 2)
“This real-time, session-based feedback loop, implemented by a hardware-implemented recommendation module as supported at least in paragraphs [0016], [0031], [0032], confers concrete technical improvements over conventional systems, including lower latency, improved system throughput, enhanced display responsiveness, and reduced computational overhead.” (Remarks page 10 par 2)
“The claimed technical features are not "well- understood, routine, or conventional" but rather provide a practical application that solves concrete technical problems in the art, such as display latency and inefficient recommendation processing.” (Remarks page 11, par2)
“The technical advantages provided include improved display latency, system throughput, and computational efficiency. These advantages are directly tied to the hardware and machine learning features recited in the claims.” (Remarks page 11, par 3)
The Examiner respectfully disagrees and notes that other than the mere allegation of providing improved display latency, system throughput, computational efficiency etc., the Applicant fails to persuasively show or demonstrate that the limitations are indicative of a practical application by showing “how” the claimed invention improves latency or how the invention improves system throughput and scalability. The Applicant fails to show an integration into a practical application by a showing of any of the following:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Further the Examiner notes that the alleged improvement of “enhanced user experience by providing real-time, adaptive recommendations” is merely an improvement to the user experience and not an improvement to any of the above mentioned bullet points such as an Improvement to the functioning of a computer, or to any other technology or technical field. The Examiner respectfully maintains the rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
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/RAW/ Examiner, Art Unit 3715
3/9/2026
/KANG HU/Supervisory Patent Examiner, Art Unit 3715