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 .
Response to Arguments
Applicant's arguments filed 01/15/2026 have been fully considered but they are not persuasive.
Regarding the 101 rejections, on pages 10-11 of “Remarks” applicant contends that the amended claim 1 does not recite mathematical abstract ideas under Step 2A Prong 1. The examiner notes that the 101 rejections for amended claim 1 does not currently recite mathematical judicial exceptions and instead recite mental process judicial exceptions. Additionally, example 39 is not applicable to this application as example 39 does not recite any judicial exceptions while this application recites judicial exceptions under the mental process group.
On pages 11-12 of “Remarks” applicant contends that the amended claim 1 does not recite mental process abstract ideas under Step 2A Prong 1. The examiner respectfully disagrees. Applicant specifically argues that the limitation “the geographical location being determined based on one or more features extracted from data content by a machine learning model” cannot be performed mentally or with pen and paper. Under the broadest reasonable interpretation, extracting geographical information from data can be performed mentally or with pen and paper and includes a step of observation, evaluation, or judgement which are mental processes (MPEP 2106). Under the broadest reasonable interpretation, using a generic machine learning model as a tool to perform judicial exceptions, represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). The limitation reciting few-shot learning or zero-shot learning is not considered a mental process and is interpreted as an additional element. Lastly, applicant argues that humans cannot mentally perform the millions of hours of steps required by the invention. First, the examiner notes that the amended limitations do not recite elements that specify that the invention is performed for millions of hours. Second, the courts have found that as long as a limitation can be performed mentally, repeated or additional steps can be performed with the help of a physical aid like pen and paper, thus repeated steps does not remove the mental nature of the limitation. See MPEP 2106.04(a)(2)(III)(B): “The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another.”
On pages 12-13 of “Remarks” applicant contends that the amended claim 1 provides a practical application under Step 2A Prong 2. The examiner respectfully disagrees. It appears that the proposed improvement is only realized because of the specific mental concepts (determining geographical context) used in the claim. The judicial exception itself cannot provide the improvement. See MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” Additionally, applicant contends that the claimed invention provides a technical solution to questionnaires by reducing the number of questions provided to the user. However, the claims do not have limitations that specify that questionnaires are being improved upon by the claimed limitations. Therefore, the additional elements do not incorporate the identified abstract ideas into a practical application. Therefore, applicant’s arguments regarding the 101 rejections are not persuasive.
On pages 13-14 of “Remarks” applicant contends that the amended claim 1 is analogous to example 48 of the subject matter eligibility examples. The examiner respectfully disagrees. Applicant’s invention is directed to the question and answering while example 48 is directed to speech analysis, thus the two inventions are not analogous inventions. Additionally, example 48 was found to be eligible due to the combination of limitations (g) and (h) detailing a technical improvement of combining speech waveforms from different sources using stitching and mixed signals. In contrast, applicant’s invention is performing judicial exceptions using a generic machine learning model as a tool to perform the judicial exceptions. The machine learning model lacks details as to how the machine learning model solves a technical problem, and instead recites only the idea of a solution or outcome.
On page 14 of “Remarks” applicant contends that the examiner incorrectly uses the “Apply It” rationale. The examiner respectfully disagrees. Under the broadest reasonable interpretation, applicant’s use of a machine learning model is interpreted as being used a tool to perform judicial exceptions and lacks details as to how the machine learning model solves a technical problem, and instead recites only the idea of a solution or outcome (MPEP 2106.05(f)). Additionally, the limitations of pre-training and using few-shot or zero-shot configurations, under the broadest reasonable interpretation, merely recites steps that apply generic computer components to perform an abstract idea which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)).
On page 15 of “Remarks” applicant contends that the amended claim 1 recites additional elements that are not well understood, routine, or conventional activities under Step 2B. The examiner respectfully disagrees. As discussed above, the amended limitations of claim 1 still recite mental process abstract ideas. Additionally, the mention of performing the identified abstract ideas using generic machine learning model, under the broadest reasonable interpretation, merely recite steps that apply generic computer components to perform an abstract idea which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Additionally, applying generic few-shot or zero-shot learning, under the broadest reasonable interpretation, merely recite steps that apply generic computer components to perform an abstract idea which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Under Step 2B, the courts have found that adding the words “apply it”, or an equivalent, with the judicial exception does not qualify as significantly more under Step 2B (MPEP 2106.05). Therefore, applicant’s arguments regarding the 101 rejections are not persuasive.
Regarding the 103 rejections, applicant's arguments filed with respect to the prior art rejections have been fully considered but they are moot. Applicant has amended the claims to recite new combinations of limitations. Applicant's arguments are directed at the amendment. Please see below for new grounds of rejection, necessitated by Amendment.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-16, and 18-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A system comprising: a memory storing instructions; and one or more hardware processors communicatively coupled to the memory and configured by the instructions to perform operations comprising:. The claim recites a system with hardware components. A system with hardware components is interpreted as an apparatus which is one of the four statutory categories of invention.
In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components:
…generate analysis of the context of the media asset based on the data content and pre-determined cultural attributes classification taxonomy associated with a geographical location of the media asset, the geographical location being determined based on one or more features extracted from the data content… (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like determining whether a context matches any prior geographical context, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
…to generate the analysis of the context of the media asset comprising: generating a task description based on the context of the media asset; identifying an example question and a set of example answers based on the task description, the pre-determined cultural attributes classification taxonomy associated with the geographical location of the media asset, and one or more features extracted from the data content that are indicative of the context of the media asset; (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like generating a description of a task, and using the task description a geolocation to identify questions and answers, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
and generating a prompt input that includes the task description, the example question, and the set of example answers; (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like generating a prompt based on a description and examples, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
to dynamically generate a question and a plurality of answers based on the prompt input, the plurality of answers being selectable for the question, (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like thinking of follow up or clarifying questions, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea.
In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
a memory storing instructions; and one or more hardware processors communicatively coupled to the memory and configured by the instructions to perform operations comprising: (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
receiving a user input from a user interface of a device, the user input including data content that describes context of a media asset; (i.e., the broadest reasonable interpretation of receiving user input is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
using a machine learning model… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
…by the machine learning model, the using of the machine learning model… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
using the machine learning model to… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
the machine learning model being pre-trained on training data (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
and configured with a prompting function to perform at least one of few-shot or zero-shot learning, (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
and causing display of the question and the plurality of answers on the user interface of the device. (i.e., the broadest reasonable interpretation of displaying answers is mere data outputting, which is an insignificant extra solution activity (MPEP 2106.05(g))).
Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea.
In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitations (VI) and (XII), under the broadest reasonable interpretation, recite steps of mere data gathering/outputting, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering/outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018)).
Examiner uses Berkheimer: Option 2, a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting well-understood, routine, and conventional nature of the additional elements:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Further, limitation (V), under the broadest reasonable interpretation, merely recites steps that apply generic computer components to perform judicial exceptions, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Similarly, limitations (VII-IX), under the broadest reasonable interpretation, merely recites steps that apply a generic machine learning model as a tool to perform judicial exceptions, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Limitation (X), under the broadest reasonable interpretation, merely recites steps that apply generic pre-training using training data, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Limitation (XI), under the broadest reasonable interpretation, merely recites steps that apply generic few-shot or zero-shot learning, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 2 recites dynamically selecting one or more recommended answers from the plurality of answers based on the analysis of the context of the media asset. Under the broadest reasonable interpretation, the limitations recite selecting a recommended answer which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 2 does not solve the deficiencies of claim 1.
Regarding claim 3, it is dependent upon claim 2 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 3 recites determining a geographical location associated with the media asset based on the data content; determining a taxonomy associated with the geographical location;…to dynamically select the one or more recommended answers based on the taxonomy and the context of the media asset. Under the broadest reasonable interpretation, the limitations recite identifying the geographical location associated with content and then selecting an answer based on the location which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Claim 3 also recites and using a machine learning model. Under the broadest reasonable interpretation, the limitations merely recite steps that apply a generic machine learning model as a tool to perform a judicial exception, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 3 does not solve the deficiencies of claim 2.
Regarding claim 4, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 4 recites wherein the machine learning model is built and trained based on prompt engineering technology. Under the broadest reasonable interpretation, the limitations merely recite steps that apply generic training of a machine learning model using prompt engineering, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 4 does not solve the deficiencies of claim 1.
Regarding claim 6, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 6 recites wherein the machine learning model is a Natural language processing (NLP) machine learning model. Under the broadest reasonable interpretation, the limitations merely recite steps that apply a generic NLP model, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 6 does not solve the deficiencies of claim 5.
Regarding claim 7, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 7 recites wherein the example question is a first example question, and wherein the set of example answers is a first set of example answers, wherein the operations further comprise: receiving, from the user interface, a selection of an answer from the plurality of answers;. Under the broadest reasonable interpretation, the limitations recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Claim 7 also recites updating the context of the media asset based on the selection of the answer; generating a second task description based on the updated context of the media asset; identifying a second example question and a second set of example answers based on the second task description and the taxonomy associated with the geographical location of the media asset; generating a second prompt input that includes the second task description, the second example question, and the second set of example answers;. Under the broadest reasonable interpretation, the limitations recite determining a new context based on the user’s answer selection, generating a new task description of the new context using the new task description, identified location, and prior questions/answers to generate a new prompt which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Claim 7 also recites using the machine learning model. Under the broadest reasonable interpretation, the limitations merely recite steps that apply a generic machine learning model as a tool to perform a judicial exception, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Claim 7 also recites to dynamically generate a second question and a second plurality of answers based on the second prompt input;. Under the broadest reasonable interpretation, the limitations recite using the new prompt to generate new questions and answers which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Claim 7 also recites and causing display of the second question and the second plurality of answers in the user interface of the device. Under the broadest reasonable interpretation, the limitations recite steps of mere data outputting, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Therefore, claim 7 does not solve the deficiencies of claim 5.
Regarding claim 8, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 8 recites wherein the context of the media asset is first context, the question is a first question, and the plurality of answers is a first plurality of answers, wherein the operations further comprise: receiving, from the user interface, a selection of an answer from the plurality of answers;. Under the broadest reasonable interpretation, the limitations recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Claim 8 also recites determining second context of the media asset based on the selection of the answer; and dynamically generating a second question based on an analysis of the second context of the media asset, the second question being associated with a second plurality of answers. Under the broadest reasonable interpretation, the limitations recite generating a new question based on an identified context related to the user’s answer selection which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 8 does not solve the deficiencies of claim 1.
Regarding claim 9, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 9 recites receiving the media asset from the device;. Under the broadest reasonable interpretation, recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Claim 9 also recites generating the user interface that includes a user response collection interface;. Under the broadest reasonable interpretation, the limitations merely recite steps that apply generic computer components, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Claim 9 also recites and causing display of the user interface on the device for receiving the user input. Under the broadest reasonable interpretation, the limitations recite steps of mere data outputting, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Therefore, claim 9 does not solve the deficiencies of claim 1.
Regarding claim 10, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 10 recites wherein the operations further comprise: receiving, from the user interface, a selection of an answer from the plurality of answers;. Under the broadest reasonable interpretation, the limitations recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Claim 10 also recites updating the context of the media asset based on the selection of the answer;…to dynamically update the plurality of answers to include one or more additional answers based on an analysis of the updated context of the media asset;. Under the broadest reasonable interpretation, the limitations recite determining a new context based on the user’s answer selection and generating an additional answer based on the new context which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Claim 10 also recites using the machine learning model. Under the broadest reasonable interpretation, the limitations merely recite steps that apply a generic machine learning model as a tool to perform a judicial exception, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Claim 10 also recites and causing display of the updated plurality of answers in the user interface of the device. Under the broadest reasonable interpretation, the limitations recite steps of mere data outputting, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Therefore, claim 10 does not solve the deficiencies of claim 1.
Regarding claim 11, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 11 recites wherein the operations further comprise: receiving, from the user interface, a selection of an answer from the plurality of answers;. Under the broadest reasonable interpretation, the limitations recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Claim 11 also recites updating the context of the media asset based on the selection of the answer; dynamically selecting one or more recommended answers from the plurality of answers based on an analysis of the updated context of the media asset;. Under the broadest reasonable interpretation, the limitations recite determining a new context based on the user’s answer selection and selecting a recommended answer based on the new context which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Claim 11 also recites and causing display of the one or more recommended answers in the user interface of the device. Under the broadest reasonable interpretation, the limitations recite steps of mere data outputting, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Therefore, claim 11 does not solve the deficiencies of claim 1.
Regarding claim 12, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 12 recites herein the operations further comprise: receiving, from the user interface, a selection of an answer from the plurality of answers;. Under the broadest reasonable interpretation, the limitations recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Claim 12 also recites updating the context of the media asset based on the selection of the answer; and generating one or more questions and selecting one or more recommended answers to each of the one or more questions based on an analysis of the updated context of the media asset. Under the broadest reasonable interpretation, the limitations recite determining a new context based on the user’s answer selection and generating new question answer pairs based on the new context which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 12 does not solve the deficiencies of claim 1.
Regarding claims 13-16 and 18-19, the claims are similar to claims 2-4 and 6-12 and are rejected under the same rationales.
Regarding claim 20, the claim is similar to claim 1. The additional limitation below fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. Claim 20 recites the additional limitation A non-transitory computer-readable medium comprising instructions that, when executed by a hardware processor of a device, cause the device to perform operations comprising:. Under the broadest reasonable interpretation, the limitations merely recite steps that apply generic computer components to perform a judicial exception, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
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.
Claims 1-4, 6, 8, 10, 12-16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Whiting, et al., US Pre-Grant Publication 2020/0349593A1 (“Whiting”) in view of Nouri, et al., US Pre-Grant Publication 2024/0038226A1 (“Nouri”) and further in view of Katz, et al., US Pre-Grant Publication 2018/0082187A1 (“Katz”), Subramanya, et al., US Patent Publication 10810193B1 (“Subramanya”), and Seeha, Non-Patent Literature “Prompt Engineering and Zero-Shot/Few-Shot Learning” (“Seeha”).
Regarding claim 1, Whiting discloses:
A system comprising: a memory storing instructions; and alone or more hardware processors communicatively coupled to the memory and configured by the instructions to perform operations comprising: (Whiting, ⁋153, “Embodiments of the present disclosure may comprise or utilize a special purpose or general-purpose computer including computer hardware, such as, for example, one or more processors and system memory [A system comprising: a memory storing instructions;]…In general, a processor (e.g., a microprocessor) receives instructions, from a non-transitory computer-readable medium, (e.g., a memory, etc.) [and one or more hardware processors communicatively coupled to the memory and configured by the instructions to perform operations comprising:]”).
receiving a user input from a user interface of a device, (Whiting, ⁋51, “In addition to providing the digital question to the respondent device 112 a, the dynamic choice reference system 104 also performs an act 204 of receiving a response from the respondent device 112 a [receiving a user input].” and Whiting, ⁋166, “In certain embodiments, the I/O interface 908 is configured to provide graphical data to a display for presentation to a user. The graphical data may be representative of one or more graphical user interfaces [from a user interface of a device,] and/or any other graphical content as may serve a particular implementation.”).
the user input including data content that describes context of a media asset; (Whiting, ⁋51, “In addition to providing the digital question to the respondent device 112 a, the dynamic choice reference system 104 also performs an act 204 of receiving a response from the respondent device 112 a; the question is interpreted as the media asset and providing a response to the question is interpreted as content that describes a context to the media asset (i.e. the user input including data content that describes context of a media asset;).”).
using a machine learning model to generate analysis of the context of the media asset based on the data content… (Whiting, ⁋26, “For instance, the dynamic choice reference system can provide a text response received from a respondent device to the machine-learning model [using a machine learning model], trained to analyze [to generate analysis of the context of the media asset based on the data content…] and select answer choices for digital survey questions”).
the plurality of answers being selectable for the question,… (Whiting, ⁋26, “Indeed, in one or more embodiments, the dynamic choice reference system provides the digital text response, the second digital survey question, the potential answer choices, and/or user embedded data to the machine-learning model. Based on the various inputs, the machine-learning model can select one or more answer choices for the second digital survey question [the plurality of answers being selectable for the question,…].”).
and causing display of the question and the plurality of answers on the user interface of the device. (Whiting, ⁋27, “Then, the dynamic choice reference system can provide the second digital survey question and the selected answer choices to the respondent device [and causing display of the question and the plurality of answers on the user interface of the device.].”).
While Whiting teaches a system for generating questions and answers based on a context, Whiting does not explicitly teach:
and pre-determined cultural attributes classification taxonomy associated with a geographical location of the media asset,
the geographical location being determined based on one or more features extracted from the data content by the machine learning model,
the using of the machine learning model to generate the analysis of the context of the media asset comprising: generating a task description based on the context of the media asset; identifying an example question and a set of example answers based on the task description, the pre-determined cultural attributes classification taxonomy associated with the geographical location of the media asset, and one or more features extracted from the data content that are indicative of the context of the media asset; and generating a prompt input that includes the task description, the example question, and the set of example answers; using the machine learning model to dynamically generate a question and a plurality of answers based on the prompt input…
the machine learning model being pre-trained on training data and configured with a prompting function to perform at least one of few-shot or zero-shot learning
Nouri teaches the using of the machine learning model to generate the analysis of the context of the media asset comprising: generating a task description based on the context of the media asset; identifying an example question and a set of example answers based on the task description,…and one or more features extracted from the data content that are indicative of the context of the media asset; and generating a…input that includes the task description, the example question, and the set of example answers; using the machine learning model to dynamically generate a question and a plurality of answers based on the…input, (Nouri, ⁋36, “information from the received task request and/or contextual information derived [,…and one or more features extracted from the data content that are indicative of the context of the media asset;] from the task related request [based on the context of the media asset;] is analyzed using a machine learning model, such as a transformer model, an LLM model, or the like…The machine learning model uses the information to generate; using the contextual information to perform an action is interpreted as generating a task description (i.e. the using of the machine learning model to generate the analysis of the context of the media asset comprising: generating a task description) a series of questions related to the task. In particular, the machine learning model generates a list of question-answer pairs that relates to the task [identifying an example question and a set of example answers based on the task description,]. In doing so, the machine learning model identifies types of information that is relevant to the task; the identified various types of information is interpreted as the input (i.e. generating a…input that includes the task description, the example question, and the set of example answers;) and generates questions and answers to the questions based upon the relevant types of information [using the machine learning model to dynamically generate a question and a plurality of answers based on the…input;].”).
Whiting and Nouri are both in the same field of endeavor (i.e. response generation). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Whiting and Nouri to teach the above limitation(s). The motivation for doing so is that using prompting techniques for question answer models improves model accuracy by providing specific instructions and conditions to the model (cf. Nouri, see ⁋2-3).
While Whiting in view of Nouri teaches a prompting system for generating questions and answers based on a context, the combination does not explicitly teach:
and pre-determined cultural attributes classification taxonomy associated with a geographical location of the media asset,
the geographical location being determined based on one or more features extracted from the data content by the machine learning model,
the pre-determined cultural attributes classification taxonomy associated with the geographical location of the media asset…prompt input that includes the task description, the example question, and the set of example answers;
the machine learning model being pre-trained on training data and configured with a prompting function to perform at least one of few-shot or zero-shot learning
Katz teaches the geographical location being determined based on one or more features extracted from the data content by the machine learning model, (Katz, ⁋4, “for evaluating the geographic relevance of an answer candidate to an input question containing geographic information based on the relationship between the geographic focus of the question [the geographical location being determined based on one or more features extracted from the data content] and the geographic focus of the answer candidate.” and Katz, ⁋21, “the “geographic focus” of a text refers to the definite geographical region which the text is about [geographical location]. For example, in “Bill Clinton was president of the USA in 1995” the geographical focus is “USA”.”, and Katz, ⁋3, “The IBM Watson™ system is an application of advanced natural language processing, information retrieval, knowledge representation and reasoning, and machine learning technologies to the field of open domain question answering [by the machine learning model,].”).
Whiting, in view of Nouri, and Katz are both in the same field of endeavor (i.e. response generation). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Whiting, in view of Nouri, and Katz to teach the above limitation(s). The motivation for doing so is that determining geographic significance of inputs/responses allows for better accuracy in generated results (cf. Katz, ⁋4, “By comparing the at least one geographic focus associated with the candidate answer with the geographic focus for the input natural language question, the QA system generates a measure of geographic relevance of the candidate answer based on results of the comparison, thereby improving the accuracy of answers”).
While Whiting in view of Nouri and Katz teaches a prompting system for generating questions and answers based on a geographical context, the combination does not explicitly teach:
and pre-determined cultural attributes classification taxonomy associated with a geographical location of the media asset,
the pre-determined cultural attributes classification taxonomy associated with the geographical location of the media asset…prompt input that includes the task description, the example question, and the set of example answers;
the machine learning model being pre-trained on training data and configured with a prompting function to perform at least one of few-shot or zero-shot learning
Subramanya teaches and pre-determined cultural attributes classification taxonomy associated with a geographical location of the media asset, (Subramanya, col. 1 background, “Large data graphs store data and rules that describe knowledge about the data in a form that provides for deductive reasoning. For example, in a data graph, entities, such as people, places, things, concepts, etc., may be stored as nodes and the edges between nodes may indicate the relationship between the nodes [and pre-determined cultural attributes classification taxonomy]. In such a data graph, the nodes “Maryland” and “United States” may be linked by the edges of “in country” and/or “has state; a data graph is interpreted as a taxonomy as it labels relationships between different concepts (i.e. associated with a geographical location of the media asset,).”).
Whiting, in view of Nouri and Katz, and Subramanya are both in the same field of endeavor (i.e. machine learning). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Whiting, in view of Nouri and Katz, and Subramanya to teach the above limitation(s). The motivation for doing so is that using a taxonomy for countries allows for more robust results as a taxonomy, or data graph, contains relationships between different concepts (cf. Subramanya, see col. 1 background).
While Whiting in view of Nouri, Katz, and Subramanya teaches a prompting system for generating questions and answers based on a geographical context, the combination does not explicitly teach:
prompt input that includes the task description, the example question, and the set of example answers;
the machine learning model being pre-trained on training data and configured with a prompting function to perform at least one of few-shot or zero-shot learning
Seeha teaches:
prompt input that includes the task description, the example question, and the set of example answers; (Seeha, pg. 2, “Prompt engineering or prompt learning is a novel approach for leveraging pre-trained language models (LMs) to perform NLP tasks without fine-tuning. In this approach, the model is informed about the target task directly through a natural language task description which is integrated into the actual input sentence in some way. The task description is called a prompt as it prompts the model to perform a specific task [prompt input].” and Seeha, pg. 4-5, “Few-shot learning is a setting where the system is given only a very small number of supervised examples (Wang et al., 2019). Few-shot usually means two to five examples per class, but it can also be up to 100 examples (Wang et al., 2021)…In prompt engineering, examples are often added directly to the prompt. Other applications in few-shot settings include parsing (Joshi et al., 2018), translation (Kaiser et al., 2017), question answering (Chada and Natarajan, 2021) [the example question, and the set of example answers;], and relation classification (Han et al., 2018).” and Seeha, pg. 7, “There are two ways to obtain the prompts: create them manually, or use an algorithm to compute them automatically. Typically, a prompt consists of three components: actual input, task description, and optionally some demonstrations; the demonstrations are interpreted as the example question-answer pairs (i.e. a prompt input that includes the task description, the example question, and the set of example answers;).”).
the machine learning model being pre-trained on training data and configured with a prompting function to perform at least one of few-shot or zero-shot learning (Seeha, pg. 2, “Prompt engineering or prompt learning is a novel approach for leveraging pre-trained language models (LMs) to perform NLP tasks without fine-tuning [the machine learning model being pre-trained on training data]. In this approach, the model is informed about the target task directly through a natural language task description which is integrated into the actual input sentence in some way. The task description is called a prompt as it prompts the model to perform a specific task.” and Seeha, pg. 4-5, “Few-shot learning is a setting where the system is given only a very small number of supervised examples (Wang et al., 2019). Few-shot usually means two to five examples per class, but it can also be up to 100 examples (Wang et al., 2021)…In prompt engineering, examples are often added directly to the prompt [and configured with a prompting function to perform at least one of few-shot or zero-shot learning,].”).
Whiting, in view of Nouri, Katz, and Subramanya, and Seeha are both in the same field of endeavor (i.e. language models). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Whiting, in view of Nouri, Katz, and Subramanya, and Seeha to teach the above limitation(s). The motivation for doing so is prompt engineering improves model performance to a task by providing examples within the prompt thus reducing the required amount of training data (cf. Seeha, pg. 12, “Prompt engineering is a powerful technique that allows us to employ a pre-trained language model for a variety of NLP tasks without fine-tuning it. Instead of fine-tuning, the model is given a natural language task description directly along with the input. This technique is particularly useful for large LMs such as GPT3, where the model is so large that fine-tuning becomes difficult or very expensive.”).
Regarding claim 2, Whiting in view of Nouri, Katz, Subramanya, and Seeha teaches the system of claim 1. Whiting further teaches wherein the operations further comprise: dynamically selecting one or more recommended answers from the plurality of answers based on the analysis of the context of the media asset. (Whiting, ⁋26, “Indeed, in one or more embodiments, the dynamic choice reference system provides the digital text response, the second digital survey question, the potential answer choices, and/or user embedded data to the machine-learning model. Based on the various inputs, the machine-learning model can select one or more answer choices for the second digital survey question [dynamically selecting one or more recommended answers from the plurality of answers based on the analysis of the context of the media asset.].”).
Regarding claim 3, Whiting in view of Nouri, Katz, Subramanya, and Seeha teaches the system of claim 2. Whiting further teaches and using a machine learning model to dynamically select the one or more recommended answers based on the taxonomy and the context of the media asset. (Whiting, ⁋123, “As used herein, the term “dynamic choice machine-learning model” refers to a machine-learning model trained to select one or more answer choices from potential answer choices for a digital question [and using a machine learning model to dynamically select the one or more recommended answers]. In particular, in some embodiments, a “dynamic choice machine-learning model” includes a machine-learning model trained to select one or more answer choices from potential answer choices for a digital question based on a response to another digital question from a respondent device and/or embedded user data [based on the taxonomy and the context of the media asset.] for a respondent.”).
Katz further teaches determining a geographical location associated with the media asset based on the data content; (Katz, ⁋4, “for evaluating the geographic relevance of an answer candidate to an input question containing geographic information based on the relationship between the geographic focus of the question [determining a geographical location associated with the media asset based on the data content;] and the geographic focus of the answer candidate.” and Katz, ⁋21, “the “geographic focus” of a text refers to the definite geographical region which the text is about [geographical location]. For example, in “Bill Clinton was president of the USA in 1995” the geographical focus is “USA”.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Katz with the teachings of Whiting in view of Nouri, Subramanya, and Seeha for the same reasons disclosed in claim 1.
Subramanya further teaches determining a taxonomy associated with the geographical location; (Subramanya, col. 1 background, “Large data graphs store data and rules that describe knowledge about the data in a form that provides for deductive reasoning. For example, in a data graph, entities, such as people, places, things, concepts, etc., may be stored as nodes and the edges between nodes may indicate the relationship between the nodes. In such a data graph, the nodes “Maryland” and “United States” may be linked by the edges of “in country” and/or “has state; a data graph is interpreted as a taxonomy as it labels relationships between different concepts (i.e. determining a taxonomy associated with the geographical location;).”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Subramanya with the teachings of Whiting in view of Nouri, Katz, and Seeha for the same reasons disclosed in claim 1.
Regarding claim 4, Whiting in view of Nouri, Katz, Subramanya, and Seeha teaches the system of claim 1. Seeha further teaches wherein the machine learning model is built and trained based on prompt engineering technology. (Seeha, pg. 2, “Prompt engineering or prompt learning is a novel approach for leveraging pre-trained language models (LMs) to perform NLP tasks without fine-tuning. In this approach, the model is informed about the target task directly through a natural language task description which is integrated into the actual input sentence in some way. The task description is called a prompt as it prompts the model to perform a specific task [wherein the machine learning model is built and trained based on prompt engineering technology.].”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Seeha with the teachings of Whiting in view of Nouri, Katz, and Subramanya for the same reasons disclosed in claim 1.
Regarding claim 6, Whiting in view of Nouri, Katz, Subramanya, and Seeha teaches the system of claim 1. Seeha further teaches wherein the machine learning model is a Natural language processing (NLP) machine learning model. (Seeha, pg. 2, “Prompt engineering or prompt learning is a novel approach for leveraging pre-trained language models (LMs) to perform NLP tasks [wherein the machine learning model is a Natural language processing (NLP) machine learning model.] without fine-tuning. In this approach, the model is informed about the target task directly through a natural language task description which is integrated into the actual input sentence in some way. The task description is called a prompt as it prompts the model to perform a specific task.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Seeha with the teachings of Whiting in view of Nouri, Katz, and Subramanya for the same reasons disclosed in claim 1.
Regarding claim 8, Whiting in view of Nouri, Katz, Subramanya, and Seeha teaches the system of claim 1. Whiting further teaches:
wherein the context of the media asset is first context, the question is a first question, and the plurality of answers is a first plurality of answers, wherein the operations further comprise: receiving, from the user interface, a selection of an answer from the plurality of answers; (Whiting, ⁋75, “For instance, as illustrated in FIG. 4A, the dynamic choice reference system 104 provides the second digital question; the second digital question is interpreted as the first question as it was generated from a first context/response (i.e. wherein the context of the media asset is first context, the question is a first question,) with the selected answer choices 414 [and the plurality of answers is a first plurality of answers,] to the respondent device 112 a. Furthermore, the dynamic choice reference system 104 can receive responses to digital questions provided with selected answer choices to select answer choices for other digital questions. For example, in one or more embodiments, the dynamic choice reference system 104 further receives a response to the digital question comprising the selected answer choices 414 from the respondent device 112 a [wherein the operations further comprise: receiving, from the user interface, a selection of an answer from the plurality of answers;]”).
determining second context of the media asset based on the selection of the answer; and dynamically generating a second question based on an analysis of the second context of the media asset, the second question being associated with a second plurality of answers. (Whiting, ⁋75, “Furthermore, the dynamic choice reference system 104 can receive responses to digital questions provided with selected answer choices to select answer choices for other digital questions. For example, in one or more embodiments, the dynamic choice reference system 104 further receives a response to the digital question comprising the selected answer choices 414 from the respondent device 112 a; this section explains that a third, fourth,...etc. digital questions/answers in a chain are generated based on the same techniques used for generating the second digital question/answers. See previous citations in claim 1 for support of generating the second digital question/answers (i.e. determining second context of the media asset based on the selection of the answer; and dynamically generating a second question based on an analysis of the second context of the media asset, the second question being associated with a second plurality of answers.)”).
Regarding claim 10, Whiting in view of Nouri, Katz, Subramanya, and Seeha teaches the system of claim 1. Whiting further teaches:
wherein the operations further comprise: receiving, from the user interface, a selection of an answer from the plurality of answers; (Whiting, ⁋75, “For instance, as illustrated in FIG. 4A, the dynamic choice reference system 104 provides the second digital question with the selected answer choices 414 to the respondent device 112 a. Furthermore, the dynamic choice reference system 104 can receive responses to digital questions provided with selected answer choices to select answer choices for other digital questions. For example, in one or more embodiments, the dynamic choice reference system 104 further receives a response to the digital question comprising the selected answer choices 414 from the respondent device 112 a [wherein the operations further comprise: receiving, from the user interface, a selection of an answer from the plurality of answers;]”).
updating the context of the media asset based on the selection of the answer; using a machine learning model to dynamically update the plurality of answers to include one or more additional answers based on an analysis of the updated context of the media asset; and causing display of the updated plurality of answers in the user interface of the device. (Whiting, ⁋75, “Furthermore, the dynamic choice reference system 104 can receive responses to digital questions provided with selected answer choices to select answer choices for other digital questions. For example, in one or more embodiments, the dynamic choice reference system 104 further receives a response to the digital question comprising the selected answer choices 414 from the respondent device 112 a; this section explains that a third, fourth,...etc. digital questions/answers in a chain are generated based on the same techniques used for generating the second digital question/answers. See previous citations in claim 1 for support of generating the second digital question/answers (i.e. updating the context of the media asset based on the selection of the answer; using a machine learning model to dynamically update the plurality of answers to include one or more additional answers based on an analysis of the updated context of the media asset; and causing display of the updated plurality of answers in the user interface of the device.)”).
Regarding claim 12, Whiting in view of Nouri, Katz, Subramanya, and Seeha teaches the system of claim 1. Whiting further teaches:
wherein the operations further comprise: receiving, from the user interface, a selection of an answer from the plurality of answers; (Whiting, ⁋75, “For instance, as illustrated in FIG. 4A, the dynamic choice reference system 104 provides the second digital question with the selected answer choices 414 to the respondent device 112 a. Furthermore, the dynamic choice reference system 104 can receive responses to digital questions provided with selected answer choices to select answer choices for other digital questions. For example, in one or more embodiments, the dynamic choice reference system 104 further receives a response to the digital question comprising the selected answer choices 414 from the respondent device 112 a [wherein the operations further comprise: receiving, from the user interface, a selection of an answer from the plurality of answers;]”).
updating the context of the media asset based on the selection of the answer; and generating one or more questions and selecting one or more recommended answers to each of the one or more questions based on an analysis of the updated context of the media asset. (Whiting, ⁋75, “Furthermore, the dynamic choice reference system 104 can receive responses to digital questions provided with selected answer choices to select answer choices for other digital questions. For example, in one or more embodiments, the dynamic choice reference system 104 further receives a response to the digital question comprising the selected answer choices 414 from the respondent device 112 a; this section explains that a third, fourth,...etc. digital questions/answers in a chain are generated based on the same techniques used for generating the second digital question/answers. See previous citations in claim 1 for support of generating the second digital question/answers (i.e. updating the context of the media asset based on the selection of the answer; and generating one or more questions and selecting one or more recommended answers to each of the one or more questions based on an analysis of the updated context of the media asset.)”).
Regarding claims 13-16, they are similar to claims 1-4. Therefore, they are rejected under the same rationales.
Regarding claim 18, the claim is similar to claim 6. Therefore, the claim is rejected under the same rationale.
Regarding claim 20, the claim is similar to claim 1. Whiting further teaches the additional limitations A non-transitory computer-readable medium comprising instructions that, when executed by a hardware processor of a device, cause the device to perform operations comprising: (Whiting, ⁋153, “Embodiments of the present disclosure may comprise or utilize a special purpose or general-purpose computer including computer hardware, such as, for example, one or more processors and system memory…In general, a processor (e.g., a microprocessor) receives instructions, from a non-transitory computer-readable medium, (e.g., a memory, etc.) [A non-transitory computer-readable medium comprising instructions that, when executed by a hardware processor of a device, cause the device to perform operations comprising:]”).
Claims 7 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Whiting, et al., US Pre-Grant Publication 2020/0349593A1 (“Whiting”) in view of Nouri, et al., US Pre-Grant Publication 2024/0038226A1 (“Nouri”) and further in view of Katz, et al., US Pre-Grant Publication 2018/0082187A1 (“Katz”), Subramanya, et al., US Patent Publication 10810193B1 (“Subramanya”), Seeha, Non-Patent Literature “Prompt Engineering and Zero-Shot/Few-Shot Learning” (“Seeha”), and Baeuml, et al., US Pre-Grant Publication 2025/0037711A1 (“Baeuml”).
Regarding claim 7, Whiting in view of Nouri, Katz, Subramanya, and Seeha teaches the system of claim 1. Katz and Subramanya also teaches and the taxonomy associated with the geographical location of the media asset as seen in claim 1.
Seeha further teaches:
wherein the example question is a first example question, and wherein the set of example answers is a first set of example answers, (Seeha, pg. 4, “Few-shot learning is a setting where the system is given only a very small number of supervised examples (Wang et al., 2019). Few-shot usually means two to five examples per class, but it can also be up to 100 examples (Wang et al., 2021); the mention of having multiple examples for different classes and having multiple examples is interpreted as having different sets of examples (i.e. wherein the example question is a first example question, and wherein the set of example answers is a first set of example answers,)”).
generating a second prompt input that includes…second task description,…second example question, and…second set of example answers; (Seeha, pg. 4, “Few-shot learning is a setting where the system is given only a very small number of supervised examples (Wang et al., 2019). Few-shot usually means two to five examples per class, but it can also be up to 100 examples (Wang et al., 2021); the mention of having multiple examples for different classes and having multiple examples is interpreted as having different sets of examples (i.e. …second example question, and…second set of example answers;)” and Seeha, pg. 7, “There are two ways to obtain the prompts: create them manually, or use an algorithm to compute them automatically. Typically, a prompt consists of three components: actual input, task description, and optionally some demonstrations; the demonstrations are interpreted as the example question-answer pairs (i.e. generating a second prompt input that includes…second task description,…second example question, and…second set of example answers;).”).
Nouri further teaches wherein the operations further comprise: receiving, from the user interface, a selection of an answer from the plurality of answers; (Nouri, ⁋29, “The answer receiver 116 receives answers to the respective questions from the client computing device 102. In aspects, the answer receiver 116 receives all answers to all of the questions in the set of questions at once from the client computing device 102 [wherein the operations further comprise: receiving, from the user interface, a selection of an answer from the plurality of answers;].”).
While Whiting in view of Nouri, Katz, Subramanya, and Seeha teaches a prompting system of generating questions and answers using geographical contexts, the combination does not explicitly teach:
a updating the context of the media asset based on the selection of the answer;
generating a second task description based on the updated context of the media asset;
identifying a second example question and a second set of example answers based on the second task description…;
using the machine learning model to dynamically generate a second question and a second plurality of answers based on the second…input;
and causing display of the second question and the second plurality of answers in the user interface of the device.
Baeuml teaches:
updating the context of the media asset based on the selection of the answer; (Baeuml, ⁋79, “At block 456, the system processes the set of assistant outputs [based on the selection of the answer;] and context of the dialog session to: (1) generate a set of modified assistant outputs using one or more LLM outputs, each of the one or more LLM outputs being determined based on at least the context of the dialog session and/or one or more assistant outputs included in the set of assistant outputs; and (2) generate an additional assistant query that is related to the spoken utterance based on at least part of the context of the dialog session [updating the context of the media asset] and at least part of the assistant query included in the spoken utterance.”).
generating a second task description based on the updated context of the media asset; (Baeuml, ⁋79, “and (2) generate an additional assistant query [generating a second task description] that is related to the spoken utterance based on at least part of the context of the dialog session [based on the updated context of the media asset;] and at least part of the assistant query included in the spoken utterance.”).
identifying a second example question and a second set of example answers based on the second task description…; (Baeuml, ⁋84, “At block 460, the system processes, based on the additional assistant output [identifying a second example question] that is responsive to the additional assistant query [based on the second task description…;], the set of modified assistant outputs to generate a set of additional modified assistant outputs [and a second set of example answers].” and Baeuml, ⁋7, “but the assistant outputs included in the set of modified assistant outputs include assistant outputs that do drive the dialog session in manner that further engages the user of the client device in the dialog session by asking contextually relevant questions [a second example question]”).
generating a second…input (Baeuml, ⁋84, “The set of additional modified assistant outputs can be generated in the same or similar manner described above with respect to generating the set of modified assistant outputs, but based on the additional assistant outputs rather than the set of assistant outputs (e.g., using the one or more LLM outputs generated in the offline manner and/or using the LLM engine 150A1 and/or 150A2 in an online manner); the LLM having inputs is interpreted as a second input thus performing repeated steps with additional inputs is interpreted as a second input (i.e. generating a second…input).”).
using the machine learning model to dynamically generate a second question and a second plurality of answers based on the second...input; (Baeuml, ⁋84, “At block 460, the system processes, based on the additional assistant output [a second question] that is responsive to the additional assistant query, the set of modified assistant outputs to generate a set of additional modified assistant outputs [and a second plurality of answers].” and Baeuml, ⁋84, “The set of additional modified assistant outputs can be generated in the same or similar manner described above with respect to generating the set of modified assistant outputs, but based on the additional assistant outputs rather than the set of assistant outputs (e.g., using the one or more LLM outputs generated in the offline manner and/or using the LLM engine 150A1 and/or 150A2 in an online manner [using the machine learning model to dynamically generate]); the LLM having performing repeated steps with additional inputs is interpreted as a second input (i.e. based on the second…input;).”).
and causing display of the second question and the second plurality of answers in the user interface of the device. (Baeuml, ⁋85, “and select the given additional modified assistant output from the set of additional modified assistant outputs [and the second plurality of answers in the user interface of the device.] (or the additional assistant output as the given additional assistant output). In these implementations, the system can combine the given modified assistant output and the given additional assistant output [and causing display of the second question], and cause the given modified assistant output and the given additional assistant output to be provided for visual and/or audible presentation to the user”).
Whiting, in view of Nouri, Katz, Subramanya, and Seeha, and Baeuml are both in the same field of endeavor (i.e. response generation). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Whiting, in view of Nouri, Katz, Subramanya, and Seeha, and Baeuml to teach the above limitation(s). The motivation for doing so is that using an LLM improves the natural conversation between the user and the question answer system (cf. Baeuml, ⁋5, “Implementations described herein are directed to enabling an automated assistant to perform natural conversations with a user during a dialog session.”).
Regarding claim 19, the claim is similar to claim 7. Therefore, the claim is rejected under the same rationale.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Whiting, et al., US Pre-Grant Publication 2020/0349593A1 (“Whiting”) in view of Nouri, et al., US Pre-Grant Publication 2024/0038226A1 (“Nouri”) and further in view of Katz, et al., US Pre-Grant Publication 2018/0082187A1 (“Katz”), Subramanya, et al., US Patent Publication 10810193B1 (“Subramanya”), Seeha, Non-Patent Literature “Prompt Engineering and Zero-Shot/Few-Shot Learning” (“Seeha”), and Forrest, et al., US Pre-Grant Publication 2006/0286530A1 (“Forrest”).
Regarding claim 9, Whiting in view of Nouri, Katz, Subramanya, and Seeha teaches the system of claim 1. Whiting further teaches:
generating the user interface that includes a user response collection interface; (Whiting, ⁋166, “In certain embodiments, the I/O interface 908 is configured to provide graphical data to a display for presentation to a user. The graphical data may be representative of one or more graphical user interfaces; a graphical user interface is interpreted as a user response collection interface as the user interacts with the graphical user interface to provide responses (i.e. generating the user interface that includes a user response collection interface;) and/or any other graphical content as may serve a particular implementation.”).
and causing display of the user interface on the device for receiving the user input. (Whiting, ⁋166, “In certain embodiments, the I/O interface 908 is configured to provide graphical data to a display for presentation to a user. The graphical data may be representative of one or more graphical user interfaces [and causing display of the user interface on the device for receiving the user input.] and/or any other graphical content as may serve a particular implementation.”).
While the combination teaches a system of generating questions and answers using contexts, the combination does not explicitly teach:
wherein the operations further comprise: receiving the media asset from the device;
Forrest teaches wherein the operations further comprise: receiving the media asset from the device; (Forrest, ⁋30, “In one embodiment, the question interface as shown in FIGS. 1 and 1A would include a questioner interface 104 which allows a user or a questioner 106 via a computer or other communication device to post questions to the database 102 directly [receiving the media asset from the device;].”).
Whiting, in view of Nouri, Katz, Subramanya, and Seeha, and Forrest are both in the same field of endeavor (i.e. question and answering systems). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Whiting, in view of Nouri, Katz, Subramanya, and Seeha, and Forrest to teach the above limitation(s). The motivation for doing so is that collecting user questions/answers improves answer searching because more questions and answers provides more examples form responses from (cf. Forrest, see ⁋2-3).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Whiting, et al., US Pre-Grant Publication 2020/0349593A1 (“Whiting”) in view of Nouri, et al., US Pre-Grant Publication 2024/0038226A1 (“Nouri”) and further in view of Katz, et al., US Pre-Grant Publication 2018/0082187A1 (“Katz”), Subramanya, et al., US Patent Publication 10810193B1 (“Subramanya”), Seeha, Non-Patent Literature “Prompt Engineering and Zero-Shot/Few-Shot Learning” (“Seeha”), and Barz, et al., Non-Patent Literature “Incremental Improvement of a Question Answering System by Re-ranking Answer Candidates Using Machine Learning” (“Barz”).
Regarding claim 11, Whiting in view of Nouri, Katz, Subramanya, and Seeha teaches the system of claim 1. Whiting further teaches:
wherein the operations further comprise: receiving, from the user interface, a selection of an answer from the plurality of answers; (Whiting, ⁋75, “For instance, as illustrated in FIG. 4A, the dynamic choice reference system 104 provides the second digital question with the selected answer choices 414 to the respondent device 112 a. Furthermore, the dynamic choice reference system 104 can receive responses to digital questions provided with selected answer choices to select answer choices for other digital questions. For example, in one or more embodiments, the dynamic choice reference system 104 further receives a response to the digital question comprising the selected answer choices 414 from the respondent device 112 a [wherein the operations further comprise: receiving, from the user interface, a selection of an answer from the plurality of answers;]”).
updating the context of the media asset based on the selection of the answer; (Whiting, ⁋75, “Furthermore, the dynamic choice reference system 104 can receive responses to digital questions provided with selected answer choices to select answer choices for other digital questions. For example, in one or more embodiments, the dynamic choice reference system 104 further receives a response to the digital question comprising the selected answer choices 414 from the respondent device 112 a; receiving a response to the second digital question is interpreted as the updated context (i.e. updating the context of the media asset based on the selection of the answer;)”).
and causing display of the one or more…answers in the user interface of the device. (Whiting, ⁋27, “Then, the dynamic choice reference system can provide the second digital survey question and the selected answer choices to the respondent device [and causing display of the one or more…answers in the user interface of the device.].”).
While the combination teaches a system of generating questions and answers using contexts, the combination does not explicitly teach:
dynamically selecting one or more recommended answers from the plurality of answers based on an analysis of the updated context of the media asset;
Barz teaches:
dynamically selecting one or more recommended answers from the plurality of answers based on an analysis of the updated context of the media asset; (Barz, pg. 374 and see Algorithm 1, “Our re-ranking approach compares a user query with the top-10 results of the baseline QA system. In contrast to the initial ranking, our re-ranking takes the content of the answer candidates into account [based on an analysis of the updated context of the media asset;] instead of encoding the user query only. Our algorithm compares the text of the recent user query to each result. We include the answer text and the confidence value of the baseline system for computing a similarity estimate. Finally, we re-rank the results; reranking the previously outputted results is interpreted as recommending answers from a plurality of answers (i.e. dynamically selecting one or more recommended answers from the plurality of answers) by their similarity to the query (see Algorithm 1).”).
Whiting, in view of Nouri, Katz, Subramanya, and Seeha, and Barz are both in the same field of endeavor (i.e. question and answering systems). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Whiting, in view of Nouri, Katz, Subramanya, and Seeha, and Barz to teach the above limitation(s). The motivation for doing so is that reranking answers provided by a QA system improves the accuracy of the given answers (cf. Barz, pg. 377, “We implemented a simple re-ranking method and showed that it can effectively improve the performance of QA systems after deployment.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Dalbec, US20210204026A1 discloses recommending a media asset based on a geographic location at which that media asset was frequently consumed.
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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