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
This action is responsive to Applicant’s claims filed 07/02/2026
Claims 1-20 are currently pending and have been examined here.
Claims 1, 5, 9, 13, 16, and 19 have been amended.
Response to Arguments
Applicant’s arguments, see pages 13-14, that Mehrota fails to teach the generation and reorganization of an ordered list. Examiner respectfully disagrees. Examiner respectfully notes that the generation of an ordered list and the reorganization of such teaches this element. (Mehrota: paragraphs [0073, 89, 93, 103, 111], Fig. 5B) Applicant’s arguments are therefore unpersuasive.
Applicant’s arguments, see pages 9-12 of Applicant’s response filed 07/02/2026, with respect to the 35 U.S.C. 101 rejections have been fully considered, but they are not persuasive. Applicant argues, on page 9, that the claims do not recite a mental process. Examiner respectfully disagrees, and notes that a human could generate stochastic parameters and assign them mentally. The mere requirement to do so using a process amounts to the generic computer implementation thereof. Applicant’s arguments are therefore unpersuasive.
Applicant argues, on pages 10-12, that the claims bring forth a technical benefit of load balancing across servers. Examiner respectfully disagrees. Examiner respectfully notes that the storage of information across multiple databases and the distribution of tasks more evenly amounts to an improvement to the abstract idea itself, since the benefit of not overloading a single task executor would be brought about if the distribution of storage and task execution were practiced outside the realm of the generic computer components recited. Since the claims, at best, bring forth an improvement to the abstract idea itself, the claims do not recite a technical improvement, and Applicant’s arguments are therefore unpersuasive. Furthermore, Examiner respectfully notes that the recitation of “such that programs recommended in the first ordered list are distributed across the plurality of content servers to reduce demand for accessing a content server of the plurality of content servers” amounts to the recitation of the intended result or intended function of the storage of the media content among a plurality of servers and varying of time periods therefore is not afforded patentable weight here.
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. The claims are drawn to ineligible patent subject matter, because the claims are directed to a recited judicial exception to patentability (an abstract idea), without claiming something significantly more than the judicial exception itself.
Claims are ineligible for patent protection if they are drawn to subject matter which is not within one of the four statutory categories, or, if the subject matter claimed does fall into one of the four statutory categories, the claims are ineligible if they recite a judicial exception, are directed to that judicial exception, and do not recite additional elements which amount to significantly more than the judicial exception itself. Alice Corp. v. CLS Bank Int'l, 375 U.S. ___ (2014). Accordingly, claims are first analyzed to determine whether they fall into one of the four statutory categories of patent eligible subject matter. Then, if the claims fall within one of the four statutory categories, it must be determined whether the claims are directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea). In determining whether a claim is directed to a judicial exception, the claim is first analyzed to determine whether the claim recites a judicial exception. If the claim does not recite one of these exceptions, the claim is directed to patent eligible subject matter under 35 U.S.C. 101. If the claim recites one of these exceptions, the claim is then analyzed to determine whether the claim recites additional elements that integrate the exception into a practical application of that exception. Claims which integrate the exception into a practical application of that exception are directed to patent eligible subject matter under 35 U.S.C. 101. If the claim fails to integrate the exception into a practical application of that exception, the claim is directed to an abstract idea. Finally, if the claims are directed to a judicial exception to patentability, the claims are then analyzed determine whether the claims are directed to patent eligible subject matter by reciting meaningful limitations which transform the judicial exception into something significantly more than the judicial exception itself. If they do not, the claims are not directed towards eligible subject matter under 35 U.S.C. § 101.
Regarding independent claims 1, 9, and 16 the claims are directed to one of the four statutory categories (a machine, a process, and an article of manufacture, respectively.) The claimed invention of independent claims 1, 9, and 16 is directed to a judicial exception to patentability, an abstract idea. The claims include limitations which recite elements which can be properly characterized under at least one of the following groupings of subject matter recognized as abstract ideas by MPEP 2106.04(a):
Mathematical Concepts: mathematical relationships, mathematical formulas or equations, and mathematical calculations;
Certain methods of organizing human activity: fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes: concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
Claims 1, 9, and 16, as a whole, recite the following limitations:
generating. . . a plurality of stochastic parameters for a plurality of recommendation objectives, wherein a value of one stochastic parameter indicates a probability of one recommendation objective to be selected as an operative recommendation objective; (claims 1, 9, 16; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could generate stochastic parameters of this type; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial media companies would perform this step in deciding which media content to recommend to customers)
assigning. . . the plurality of stochastic parameters to the plurality of recommendation objectives; (claims 1, 9, 16; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could assign stochastic parameters to recommendation objectives; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial media companies would perform this step in deciding which media content to recommend to customers)
associating each program of a plurality of programs for playback on a media system with one or more recommendation objectives of the plurality of recommendation objectives; (claims 1, 9, 16; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could associate programs with recommendation objectives; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial media companies would perform this step in deciding which media content to recommend to customers)
selecting, during a first recommendation time period, a first set of operative recommendation objectives from the plurality of recommendation objectives based on the plurality of stochastic parameters; (claims 1, 9, 16; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could select a first set of objectives based on stochastic parameters; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial media companies would perform this step in deciding which media content to recommend to customers)
generating, during the first recommendation time period, a first ordered list of recommended programs from the plurality of programs based on the first set of operative recommendation objectives; (claims 1, 9, 16; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could generate an ordered list based on a first set of objectives; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial media companies would perform this step in deciding which media content to recommend to customers)
wherein the plurality of stochastic parameters cause the first set of operative recommendation objectives to vary across recommendation time periods such that programs recommended in the first ordered list are distributed across the plurality of content servers to reduce demand for accessing a content server of the plurality of content servers; (Claims 1, 9, 16; the broadest reasonable interpretation of this limitation merely alters the type of parameters used in the abstract idea and therefore further recites one or more abstract ideas for the reasons outlined above; furthermore, Examiner respectfully notes that the recitation of “such that programs recommended in the first ordered list are distributed across the plurality of content servers to reduce demand for accessing a content server of the plurality of content servers” amounts to the recitation of the intended result or intended function of the storage of the media content among a plurality of servers and varying of time periods therefore is not afforded patentable weight here)
displaying, during the first recommendation time period, the plurality of programs in a list. . . (claims 1, 9, 16; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could display recommended media programs in a list; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial media companies would perform this step in deciding which media content to recommend to customers)
and dynamically reorganizing, during the first recommendation time period, programs displayed . . . based on the first ordered list of recommended programs. (claims 1, 9, 16; the broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could reorganize programs displayed based on a first list; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial media companies would perform this step in deciding which media content to recommend to customers)
The above elements, as a whole, recite mental processes since, but for the requirement to implement the above set of steps on a generic computer component, the entirety of the above set of steps could be performed by a human using their mind, pen and paper, and simple observation, evaluation, and judgment. Furthermore, as a whole, the broadest reasonable interpretation of the above set of steps certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial media companies would perform these steps in deciding which media content to recommend to customers.
Moving forward, the above recited abstract idea is not integrated into a practical application.
The added limitations do not represent an integration of the abstract idea into a practical application because:
the claims represent mere instructions to implement an abstract idea on a computer, and merely use a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).
the claims merely add insignificant extra-solution activity to the judicial exception (activity which can be characterized as incidental to the primary purpose or product that is merely a nominal or tangential addition to the claim). See MPEP 2106.05(g) and/or
the claims represent mere general linking of the use of the judicial exception to a particular technological environment or field of use. See MPEP 2016.05(h)
Beyond those limitations which recite the abstract idea, the following limitations are added:
A computer-implemented method for stochastic multi-period multi-objective optimization based recommendation systems, comprising: (claim 1; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use)
by at least one computer processor (claim 1; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use)
A system, comprising: (claim 9; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use)
a memory; (claim 9; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use)
and one or more processors coupled to the memory and configured to: (claim 9; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use)
A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising: (claim 16; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use)
on a graphical user interface (GUI) (claims 1, 9, 16; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use)
wherein the plurality of programs are stored across a plurality of content servers, and wherein the plurality of stochastic parameters cause the first set of operative recommendation objectives to vary across recommendation time periods such that programs recommended in the first ordered list are distributed across the plurality of content servers to reduce demand for accessing a content server of the plurality of content servers; (claims 1, 9, 16; the broadest reasonable interpretation of this limitation represents mere instructions to implement the abstract idea on a generic computer used as a tool in its ordinary capacity; alternatively, the broadest reasonable interpretation of this limitation represents mere general linking of the abstract idea to a particular computer environment or field of use; furthermore, Examiner respectfully notes that the recitation of “such that programs recommended in the first ordered list are distributed across the plurality of content servers to reduce demand for accessing a content server of the plurality of content servers” amounts to the recitation of the intended result or intended function of the storage of the media content among a plurality of servers and varying of time periods therefore is not afforded patentable weight here)
The claims, as a whole, are directed to the abstract idea(s) which they recite. The claim limitations do not present improvements to another technological field, nor do they improve the functioning of a computer or another technology. Nor do the claim limitations apply the judicial exception with, or by use of a particular machine. The claims do not effect a transformation or reduction of a particular article to a different state or thing. See MPEP 2106.05(c). None of the hardware in the claims "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment' that is, implementation via computers” such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP 2106.05(e); Alice Corp. v. CLS Bank Int’l (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)). Therefore, because the claims recite a judicial exception (an abstract idea) and do not integrate the judicial exception into a practical application, the claims, as a whole, are directed to the judicial exception.
Turning to the final prong of the test (Step 2B), independent claims 1, 9, and 16 do not include additional elements that are sufficient to amount to significantly more than the judicial exception, because there are no meaningful limitations which transform the exception into a patent eligible application. T
As outlined above, the claim limitations do not present improvements to another technological field, nor do they improve the functioning of a computer or another technology. Nor do the claim limitations apply the judicial exception with, or by use of a particular machine. The claims do not effect a transformation or reduction of a particular article to a different state or thing. See MPEP 2106.05(c). None of the hardware in the claims "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment' that is, implementation via computers” such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP 2106.05(e); Alice Corp. v. CLS Bank Int’l (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
Furthermore, no specific limitations are added which represent something other than what is well-understood, routine, and conventional activity in the field. See MPEP 2106.05(d). Besides performing the abstract idea itself, the generic computer components only serve to perform the court-recognized well-understood computer functions of receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory. See MPEP 2106.05(d). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation. The specification details any combination of a generic computer system program to perform the method. Generically recited computer elements do not add a meaningful limitation to the abstract idea because they would be routine in any computer implementation and because the Alice decision noted that generic structures that merely apply the abstract ideas are not significantly more than the abstract ideas. Therefore, independent claims 1, 9, and 16 are rejected under 35 U.S.C. §101 as being directed to ineligible subject matter.
Claims 2-8, 10-15, and 17-20, recite the same abstract idea as their respective independent claims.
The following additional features are added in the dependent claims:
Claims 2, 10, and 17:
selecting, during a second recommendation time period, a second set of operative recommendation objectives from the plurality of recommendation objectives based on the plurality of stochastic parameters;
and generating, during the second recommendation time period, a second ordered list of recommended programs from the plurality of programs based on the second set of operative recommendation objectives.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could select a second set of recommendation objectives, and generate a second ordered list based on them; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial media companies would perform this step in deciding which media content to recommend to customers.
Claims 3, 11, and 18:
wherein the generating the first ordered list of recommended programs further comprises: obtaining an existing ordered list of ranked recommended programs;
identifying one or more programs of the existing ordered list that are associated with one or more recommendation objectives of the first set of operative recommendation objectives;
generating the first ordered list by boosting the rank of the one or more programs of the existing ordered list.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could obtain an existing ordered list, identify programs associated with objectives, and boost the rank of those programs; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial media companies would perform this step in deciding which media content to recommend to customers.
Claims 4 and 12:
wherein the boosting the rank of the one or more programs is performed using a reciprocal ranking technique.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could use a reciprocal ranking technique to boost rankings; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial media companies would perform this step in deciding which media content to recommend to customers.
Claims 5, 13, and 19:
wherein the generating the first ordered list of recommended programs further comprises: assigning a respective set of weights to the first set of operative recommendation objectives;
identifying one or more operative recommendation objectives of the first set of operative recommendation objectives associated with each program of the plurality of programs;
assigning a stochastic cumulative score for each program of the plurality of programs based on the one or more operative recommendation objectives associated with each program of the plurality of programs;
generating the first ordered list by sorting the plurality of programs based on the stochastic cumulative score assigned to each program of the plurality of programs.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could assign weights to objectives, identify operative objectives, assign a stochastic cumulative score and generate an ordered list by sorting the programs based on the stochastic cumulative score; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial media companies would perform this step in deciding which media content to recommend to customers.
Claims 6 and 14:
wherein the stochastic cumulative score for each program is a weighted average score.
The broadest reasonable interpretation of this limitation recites mental processes since a human using their mind, pen and paper, and simple observation, evaluation, and judgment could perform the steps of claim 5 using a weighted average score; alternatively, the broadest reasonable interpretation of this limitation recites certain methods of organizing human activity in the form of commercial interactions such as business relations and sales activities since commercial media companies would perform this step in deciding which media content to recommend to customers.
Claims 7, 15, and 20:
wherein a stochastic parameter of the plurality of stochastic parameters associated with a recommendation objective of the plurality of recommendation objectives determines the probability that the recommendation objective is selected as an operative recommendation objective during the respective recommendation time period.
The broadest reasonable interpretation of this limitation merely alters the stochastic parameter used in the abstract idea above, and therefore further recites one or more abstract ideas for the reasons outlined above.
Claim 8:
wherein the plurality of recommendation objectives include one or more of the following: user engagement, revenue, click-through rate, program play rate, and program streaming time.
The broadest reasonable interpretation of this limitation merely alters the objectives used in the abstract idea above, and therefore further recites one or more abstract ideas for the reasons outlined above.
The above limitations do not represent a practical application of the recited abstract idea. The claim limitations do not present improvements to another technological field, nor do they improve the functioning of a computer or another technology. Nor do the claim limitations apply the judicial exception with, or by use of a particular machine. The claims do not effect a transformation or reduction of a particular article to a different state or thing. See MPEP 2106.05(c). None of the hardware in the claims "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment' that is, implementation via computers” such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP 2106.05(e); Alice Corp. v. CLS Bank Int’l (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)). Therefore, because the claims recite a judicial exception (an abstract idea) and do not integrate the judicial exception into a practical application, the claims are also directed to the judicial exception.
Furthermore, the added limitations do not direct the claim to significantly more than the abstract idea. No specific limitations are added which represent something other than what is well-understood, routine, and conventional activity in the field. See MPEP 2106.05(d). Accordingly, none of the dependent claims 2-8, 10-15, and 17-20, individually, or as an ordered combination, are directed to patent eligible subject matter under 35 U.S.C. 101.
Please see MPEP §2106.05(d)(II) for a discussion of elements that the Courts have recognized as well-understood, routine, conventional, activity in particular fields.
Please see MPEP §2106 for examination guidelines regarding patent subject matter eligibility.
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 (i.e., changing from AIA to pre-AIA ) 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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5-11, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mehrotra et al. (U.S. PG Pub. No. 20220019922; hereinafter "Mehrotra") in view of Yang, Tailong, Shuyan Zhang, and Cuixia Li. ("A multi-objective hyper-heuristic algorithm based on adaptive epsilon-greedy selection." Complex & Intelligent Systems 7.2 (2021): 765-780; hereinafter "Yang").
As per claim 1, Mehrotra teaches:
A computer-implemented method for stochastic multi-period multi-objective optimization based recommendation systems, comprising:
Mehrotra teaches a multi-objective media content distribution system and method. (Mehrotra: abstract)
With respect to the following limitation:
generating, by at least one computer processor, a plurality of stochastic parameters for a plurality of recommendation objectives, wherein a value of one stochastic parameter indicates a probability of one recommendation objective to be selected as an operative recommendation objective;
Mehrotra teaches implementing the system and method on a processor which executes code stored in a physical memory in order to perform the functions of the system. (Mehrotra: paragraph [0022-29], Fig. 1) Mehrotra teaches that multiple objectives may be balanced by the system in order to recommend media content to users. (Mehrotra: paragraph [0020-21, 104-105]) Mehrotra, however, does not appear to explicitly teach a stochastic parameter used to select an objective to be evaluated.
Yang, however, teaches that an objective function to be accomplished in the form of a low level heuristic may be selected based on a greedy epsilon algorithm which assigns an epsilon value between 0 and 1 (a generated stochastic parameter) to the LLH, wherein the epsilon value represents the probability of the LLH being selected for use in the evaluation. (Yang: "Adaptive Epsilon-greed selection strategy") Yang teaches combining the above elements with the teachings of Mehrotra for the benefit of improving decision making ability of a model and removing the need to manually tune an epsilon value, and allowing for exploring more objectives early and then focus on getting greedier as a model progresses. Id. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Yang with the teachings of Mehrotra to achieve the aforementioned benefits.
Mehrotra in view of Yang further teaches:
assigning, by at least one computer processor, the plurality of stochastic parameters to the plurality of recommendation objectives;
Mehrotra teaches implementing the system and method on a processor which executes code stored in a physical memory in order to perform the functions of the system. (Mehrotra: paragraph [0022-29], Fig. 1) Mehrotra teaches that multiple objectives may be balanced by the system in order to recommend media content to users. (Mehrotra: paragraph [0020-21, 104-105]) Yang, as outlined above, teaches that an objective function to be accomplished in the form of a low level heuristic may be selected based on a greedy epsilon algorithm which assigns an epsilon value between 0 and 1 to the LLH, wherein the epsilon value represents the probability of the LLH being selected for use in the evaluation. (Yang: "Adaptive Epsilon-greed selection strategy") The motivation to combine Yang persists.
associating each program of a plurality of programs for playback on a media system with one or more recommendation objectives of the plurality of recommendation objectives;
Mehrotra teaches that a reward vector indicating how well a set of songs satisfies each objective may be generated by the system. (Mehrotra: paragraph [0083-85], Fig. 5B) Mehrota further teaches that the media content is for playback on the device. (Mehrota: paragraphs [0042-44])
selecting, during a first recommendation time period, a first set of operative recommendation objectives from the plurality of recommendation objectives based on the plurality of stochastic parameters;
Yang, as outlined above, teaches that an objective function to be accomplished in the form of a low level heuristic may be selected based on a greedy epsilon algorithm which assigns an epsilon value between 0 and 1 to the LLH, wherein the epsilon value represents the probability of the LLH being selected for use in the evaluation. (Yang: "Adaptive Epsilon-greed selection strategy") The motivation to combine Yang persists. Mehrotra further teaches that the algorithm may be run for the current time period of recommendation of media content to the user. (Mehrotra: paragraph [0073, 93])
generating, during the first recommendation time period, a first ordered list of recommended programs from the plurality of programs based on the first set of operative recommendation objectives;
Mehrotra teaches that multiple arms may be generated, wherein each arm comprises a list of media items to play to a user, and wherein each media item may be associated with a given objective. (Mehrotra: paragraph [0073, 81-85, 93])
wherein the plurality of programs are stored across a plurality of content servers, and wherein the plurality of stochastic parameters cause the first set of operative recommendation objectives to vary across recommendation time periods such that programs recommended in the first ordered list are distributed across the plurality of content servers to reduce demand for accessing a content server of the plurality of content servers;
Mehrota further teaches that the plurality of programs may be stored across a plurality of media content servers. (Mehrota: paragraphs [0026, 28, 32-33, 69]) Mehrota further teaches that the objective may vary based on the time of day. (Mehrota: paragraph [0075, 107]) Mehrotra alternatively teaches varying objectives by time period since Mehrota teaches that the GUI may be updated at the time the user is using the system to display the list of content items which have been selected. (Mehrotra: paragraph [0073, 93, 103, 111], Fig. 5B) Mehrotra further teaches that this process may be repeated for a second list of media content items for a second user session (a second time period) using an updated matrix. (Mehrotra: paragraphs [0111-117], Fig. 5B)
Examiners Note: Examiner respectfully notes that the recitation of “such that programs recommended in the first ordered list are distributed across the plurality of content servers to reduce demand for accessing a content server of the plurality of content servers” amounts to the recitation of the intended result or intended function of the storage of the media content among a plurality of servers and varying of time periods therefore is not afforded patentable weight here.
displaying, during the first recommendation time period, the plurality of programs in a list on a graphical user interface (GUI) of the media system;
Mehrotra teaches that the GUI may be updated at the time the user is using the system to display the list of content items which have been selected. (Mehrotra: paragraph [0073, 93, 103, 111], Fig. 5B)
and dynamically reorganizing, during the first recommendation time period, the plurality of programs displayed on the GUI based on the first ordered list of recommended programs.
Mehrotra teaches that the GUI may be updated at the time the user is using the system to display the list of content items which have been selected. (Mehrotra: paragraph [0073, 93, 103, 111], Fig. 5B)
As per claim 2, Mehrotra in view of Yang teaches all of the limitations of claim 1, as outlined above, and further teaches:
selecting, during a second recommendation time period, a second set of operative recommendation objectives from the plurality of recommendation objectives based on the plurality of stochastic parameters;
Mehrotra teaches that the GUI may be updated at the time the user is using the system to display the list of content items which have been selected. (Mehrotra: paragraph [0073, 93, 103, 111], Fig. 5B) Mehrotra further teaches that this process may be repeated for a second list of media content items for a second user session using an updated matrix. (Mehrotra: paragraphs [0111-117], Fig. 5B)
and generating, during the second recommendation time period, a second ordered list of recommended programs from the plurality of programs based on the second set of operative recommendation objectives.
Mehrotra teaches that the GUI may be updated at the time the user is using the system to display the list of content items which have been selected. (Mehrotra: paragraph [0073, 93, 103, 111], Fig. 5B) Mehrotra further teaches that this process may be repeated for a second list of media content items for a second user session using an updated matrix. (Mehrotra: paragraphs [0111-117], Fig. 5B)
As per claim 3, Mehrotra in view of Yang teaches all of the limitations of claim 1, as outlined above, and further teaches:
wherein the generating the first ordered list of recommended programs further comprises: obtaining an existing ordered list of ranked recommended programs;
Mehrotra teaches that the user may select a given media content item, wherein doing so changes the reward matrix given to the item with respect to the objectives, and wherein changing the reward in a positive manner boosts the probability that the list may be selected for display to a user. (Mehrotra: paragraph [0113-116])
identifying one or more programs of the existing ordered list that are associated with one or more recommendation objectives of the first set of operative recommendation objectives;
Mehrotra teaches that the user may select a given media content item, wherein doing so changes the reward matrix given to the item with respect to the objectives, and wherein changing the reward in a positive manner boosts the probability that the list may be selected for display to a user. (Mehrotra: paragraph [0113-116])
generating the first ordered list by boosting the rank of the one or more programs of the existing ordered list.
Mehrotra teaches that the user may select a given media content item, wherein doing so changes the reward matrix given to the item with respect to the objectives, and wherein changing the reward in a positive manner boosts the probability that the list may be selected for display to a user. (Mehrotra: paragraph [0113-116])
As per claim 5, Mehrotra in view of Yang teaches all of the limitations of claim 1, as outlined above, and further teaches:
wherein the generating the first ordered list of recommended programs further comprises: assigning a respective set of weights to the first set of operative recommendation objectives;
Mehrotra teaches that a generalized Gini function (GGF) may be used to apply weights to each objective, wherein the GGI may be used to determine a probabilistic (stochastic) score indicating the extent to which the arm meets objectives, and indicating the probability of the arm being selected for display to the user. (Mehrotra: paragraph [0087-93])
identifying one or more operative recommendation objectives of the first set of operative recommendation objectives associated with each program of the plurality of programs;
Mehrotra teaches that a generalized Gini function (GGF) may be used to apply weights to each objective, wherein the GGI may be used to determine a probabilistic (stochastic) score indicating the extent to which the arm meets objectives, and indicating the probability of the arm being selected for display to the user. (Mehrotra: paragraph [0087-93])
assigning a stochastic cumulative score for each program of the plurality of programs based on the one or more operative recommendation objectives associated with each program of the plurality of programs;
Mehrotra teaches that a generalized Gini function (GGF) may be used to apply weights to each objective, wherein the GGI may be used to determine a probabilistic (stochastic) score indicating the extent to which the arm meets objectives, and indicating the probability of the arm being selected for display to the user. (Mehrotra: paragraph [0087-93])
generating the first ordered list by sorting the plurality of programs based on the stochastic cumulative score assigned to each program of the plurality of programs.
Mehrotra teaches that a generalized Gini function (GGF) may be used to apply weights to each objective, wherein the GGI may be used to determine a probabilistic (stochastic) score indicating the extent to which the arm meets objectives, and indicating the probability of the arm being selected for display to the user. (Mehrotra: paragraph [0087-93])
As per claim 6, Mehrotra in view of Yang teaches all of the limitations of claim 5, as outlined above, and further teaches:
wherein the stochastic cumulative score for each program is a weighted average score.
Mehrotra teaches that a generalized Gini function (GGF) may be used to apply weights to each objective, wherein the GGI may be used to determine a probabilistic (stochastic) score indicating the extent to which the arm meets objectives, and indicating the probability of the arm being selected for display to the user. (Mehrotra: paragraph [0087-93])
As per claim 7, Mehrotra in view of Yang teaches all of the limitations of claim 1, as outlined above, and further teaches:
wherein a stochastic parameter of the plurality of stochastic parameters associated with a recommendation objective of the plurality of recommendation objectives determines the probability that the recommendation objective is selected as an operative recommendation objective during the respective recommendation time period.
Yang, as outlined above, teaches that an objective function to be accomplished in the form of a low level heuristic may be selected based on a greedy epsilon algorithm which assigns an epsilon value between 0 and 1 to the LLH, wherein the epsilon value represents the probability of the LLH being selected for use in the evaluation. (Yang: "Adaptive Epsilon-greed selection strategy") The motivation to combine Yang persists. Mehrotra further teaches that the algorithm may be run for the current time period of recommendation of media content to the user. (Mehrotra: paragraph [0073, 93])
As per claim 8, Mehrotra in view of Yang teaches all of the limitations of claim 1, as outlined above, and further teaches:
wherein the plurality of recommendation objectives include one or more of the following: user engagement, revenue, click-through rate, program play rate, and program streaming time.
Mehrotra teaches that the objective may comprise duration of songs played (program streaming time) and user engagement objectives. (Mehrotra: paragraph [0067])
As per claim 9, Mehrotra in view of Yang teaches the limitations of this claim which are substantially identical to those of claim 1, as outlined above, and further teaches:
A system, comprising:
Mehrotra teaches a multi-objective media content distribution system and method. (Mehrotra: abstract)
a memory;
Mehrotra teaches implementing the system and method on a processor which executes code stored in a physical memory in order to perform the functions of the system. (Mehrotra: paragraph [0022-29], Fig. 1)
and one or more processors coupled to the memory and configured to:
Mehrotra teaches implementing the system and method on a processor which executes code stored in a physical memory in order to perform the functions of the system. (Mehrotra: paragraph [0022-29], Fig. 1)
As per claims 10-11 and 13-15, Mehrotra in view of Yang teaches the limitations of these claims which are substantially identical to those of claims 2-3 and 5-7, and claims 10-11 and 13-15 are rejected for the same reasons as claims 2-3 and 5-7, as outlined above.
As per claim 16, Mehrotra in view of Yang teaches the limitations of this claim which are substantially identical to those of claim 1, as outlined above, and further teaches:
A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:
Mehrotra teaches a multi-objective media content distribution system and method. (Mehrotra: abstract) Mehrotra teaches implementing the system and method on a processor which executes code stored in a physical memory in order to perform the functions of the system. (Mehrotra: paragraph [0022-29], Fig. 1)
As per claims 17-18, 19, and 20, Mehrotra in view of Yang teaches the limitations of these claims which are substantially identical to those of claims 2-3, 5, and 7, and claims 17-18, 19, and 20, are rejected for the same reasons as claims 2-3 and 5, and 7, respectively, as outlined above.
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Mehrotra in view of Yang further in view of Carmichael et al. (U.S. PG Pub. No. 20160316268; hereinafter "Carmichael").
As per claim 4, Mehrotra in view of Yang teaches all of the limitations of claim 3, as outlined above. With respect to the following limitation:
wherein the boosting the rank of the one or more programs is performed using a reciprocal ranking technique.
Mehrotra teaches that the user may select a given media content item, wherein doing so changes the reward matrix given to the item with respect to the objectives, and wherein changing the reward in a positive manner boosts the probability that the list may be selected for display to a user. (Mehrotra: paragraph [0113-116]) Mehrotra, however, does not appear to teach reciprocal ranking used to boost this probability.
Carmichael, however, teaches that indications that other users have selected and watched a content item (a reciprocal ranking technique) may affect whether a given content item is suggested to a user. (Carmichael: paragraphs [0089, 159, 228-233], Fig. 5) Carmichael teaches combining the aforementioned elements with the teachings of Mehrotra in view of Yang for the benefit of allowing users to efficiently navigate content selections and easily identify content that they may desire. (Carmichael: paragraph [0092]) Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Carmichael with the teachings of Mehrotra to achieve the aforementioned benefits.
As per claim 12, Mehrotra in view of Yang further in view of Carmichael teaches the limitations of this claim which are substantially identical to those of claim 4, and claim 12 is rejected for the same reasons as claim 4, as outlined above.
Conclusion
THIS ACTION IS MADE FINAL. 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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/EMMETT K. WALSH/Primary Examiner, Art Unit 3628
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