The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of Claims
This action is in reply to the amendments and arguments filed on June 4, 2025.
The instant application claims priority to Japanese patent application JP2020-050045 filed March 19, 2020.
Claims 1-9 are currently pending.
Claims 1-9 have been amended.
Specification
The amended title of the invention is accepted.
Claim Interpretation
Applicant has amended the claims such that they no longer invoke 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 112
The previous rejection of claim 6 under 35 U.S.C. 112(b) is withdrawn in view of Applicant’s amendment.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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g. mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined that step 2A, Prong that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
According to Step 1 of the analysis, in the instant case claims 1-7 are directed to a device, claim 8 is directed to a method, and claim 9 is directed to a non-transitory computer readable storage medium. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Considering claim 1 and Step 2A, Prong One, the limitations including: “a generation model which generates modified content in which a part of content is modified in a modifying manner based on a modification policy to generate the modified content”, “evaluate, by using an evaluation model which evaluates whether inputted content is content in which modification matching an inputted modification policy is made, whether the modified content is content in which modification matching the modification policy is made”, and “the modification policy comprises a textual input provided by a user while the user provides the content” covers performance of the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind.
MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” The “generation” limitation is an evaluation/judgment of content, and a human is capable of generating modified content based on a modification policy mentally, or with pen and paper. Further, the “evaluation” limitation of claim 1 is an evaluation that can be performed mentally, and, accordingly, the claim recites an abstract idea. Additionally, the modification policy comprising a textual input provided by a user while the user provides the content can be accomplished mentally or with pen and paper.
Considering Step 2A, Prong Two, the judicial exception in claim 1 is not integrated into a practical application. Claim 1 includes the additional elements: “[a]n evaluation device”, “a processor”, “acquir[ing] modified content”, and a “machine learning model.” The “evaluation device” and “processor” do not integrate the abstract idea into a practical application because they amount to mere instructions to implement the abstract idea on a computer (device); see MPEP 2106.05(f). The content acquisition is insignificant extra-solution activity, mere data gathering; see MPEP 2106.05(g). Further, the “machine learning model” does not integrate the judicial exception into a practical application because the additional elements are a tangential addition to the claim; see MPEP 2106.05(g).
Further, considering Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites additional elements: “[a]n evaluation device”, “a processor”, “acquir[ing] modified content”, and a “machine learning model.” The evaluation device and processor amount to generic computer components and do not amount to significantly more; see MPEP 2106.2016.05(a) and 2106.2016.05(b). Further, the modified content acquisition is insignificant extra-solution activity, mere data gathering, and does not amount to significantly more; see MPEP 2106.05(g). Lastly, the machine learning model trained using historical input data is well-understood, routine and conventional. Schreyer, U.S. Patent Application Publication 2017/0161855, discloses that “[t]he rule learning component 244 of the recommendation engine 240 may implement one or more predictive analytics techniques, such as supervised machine learning algorithms, unsupervised machine learning algorithms, and pattern recognition algorithms that are currently well-known in the art to recognize patterns in the historical training data sets 402,” in [0073]. Therefore, the limitation does not amount to significantly more and the claim is not eligible in view of 101.
Claim 2, dependent on claim 1, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more. The “perform learning of the generation model” covers performance of the mind, observations, evaluations, and judgments; see MPEP 2106.04(a)(2)(III).
This judicial exception is not integrated into a practical application. The claim does not contain any additional elements apart from the abstract idea mentioned above.
Further, the claim does not include new additional elements that are sufficient to amount to significantly more than the judicial exception. Thus, the claim is directed to an abstract idea.
Claim 3, dependent on claim 2, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more. The “reinforcement learning of the generation model is performed based on an obtained evaluation result” covers performance of the mind, observations, evaluations, and judgments; see MPEP 2106.04(a)(2)(III).
This judicial exception is not integrated into a practical application. The claim does not contain any additional elements apart from the abstract idea mentioned above.
Claim 4, dependent on claim 1, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more. The “a model which evaluates whether content is content in which modification matching a context based on the modification policy is made” covers performance of the mind, observations, evaluations, and judgments; see MPEP 2106.04(a)(2)(III).
This judicial exception is not integrated into a practical application. The claim does not contain any additional elements apart from the abstract idea mentioned above.
Claim 5, dependent on claim 1, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more. The “learning is performed so as to generate modified content in which a part of the content is modified in accordance with the context indicated by the modification policy” covers performance of the mind, observations, evaluations, and judgments; see MPEP 2106.04(a)(2)(III).
Claim 5 contains additional elements: “the modification policy and the content are inputted, the acquisition unit acquires modified content generated by a model” This judicial exception is not integrated into a practical application nor does it amount to significantly more because the elements amount to insignificant extra-solution activity, mere data gathering; see MPEP 2106.05(g). Thus, the claim is not patent eligible.
Claim 6, dependent on claim 1, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more. The “the generation model, in which learning is performed so as to generate modified content in which a part of the content is modified in accordance with the context indicated by the modification policy” covers performance of the mind, observations, evaluations, and judgments; see MPEP 2106.04(a)(2)(III).
Claim 6 contains additional elements: “the modification policy and the content are inputted, acquire the modified content generated by a model” This judicial exception is not integrated into a practical application nor does it amount to significantly more because the elements amount to insignificant extra-solution activity, mere data gathering; see MPEP 2106.05(g). Thus, the claim is not patent eligible.
Claim 7, dependent on claim 1, is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without integration into a practical application or significantly more. The “the generation model, in which learning is performed so as to generate modified content in which a part of the moving image is modified in accordance with a context indicated by the sound” covers performance of the mind, observations, evaluations, and judgments; see MPEP 2106.04(a)(2)(III).
Claim 7 contains additional elements: “wherein when sound and a moving image are inputted, acquire the modified content generated by a model” This judicial exception is not integrated into a practical application nor does it amount to significantly more because the elements amount to insignificant extra-solution activity, mere data gathering; see MPEP 2106.05(g). Thus, the claim is not patent eligible.
Considering claim 8 and Step 2A, Prong One, the limitations including: “a generation model which generates modified content in which a part of content is modified in a modifying manner based on a modification policy to generate the modified content”, “an evaluation unit which evaluates, by using an evaluation model which evaluates whether inputted content is content in which modification matching an inputted modification policy is made, whether the modified content acquired by the acquisition unit is content in which modification matching the modification policy acquired by the acquisition unit is made”, and “the modification policy comprises a textual input provided by a user while the user provides the content” covers performance of the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind.
MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” The “generation” limitation is an evaluation/judgment of content, and a human is capable of generating modified content based on a modification policy mentally, or with pen and paper. Further, the “evaluation” limitation of claim 8 is an evaluation that can be performed mentally, and, accordingly, the claim recites an abstract idea. Additionally, the modification policy comprising a textual input provided by a user while the user provides the content can be accomplished mentally or with pen and paper.
Considering Step 2A, Prong Two, the judicial exception in claim 8 is not integrated into a practical application. Claim 8 includes the additional elements: “an acquisition step” and “a machine learning model.” The “acquisition step” amounts to insignificant extra-solution activity, mere data gathering, and does not integrate the claim into a practical application; see MPEP 2106.05(g). Further, the “machine learning model” does not integrate the judicial exception into a practical application because the additional elements are a tangential addition to the claim; see MPEP 2106.05(g).
Further, considering Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The “acquisition step” amounts to insignificant extra-solution activity, mere data gathering, and does not amount to significantly more; see MPEP 2106.05(g). And, the machine learning model trained using historical input data is well-understood, routine and conventional. Schreyer, U.S. Patent Application Publication 2017/0161855, discloses that “[t]he rule learning component 244 of the recommendation engine 240 may implement one or more predictive analytics techniques, such as supervised machine learning algorithms, unsupervised machine learning algorithms, and pattern recognition algorithms that are currently well-known in the art to recognize patterns in the historical training data sets 402,” in [0073]. Therefore, the limitation does not amount to significantly more and the claim is not eligible in view of 101.
Considering claim 9 and Step 2A, Prong One, the limitations including: “a generation model which generates modified content in which a part of content is modified in a modifying manner based on a modification policy to generate the modified content”, “an evaluation unit which evaluates, by using an evaluation model which evaluates whether inputted content is content in which modification matching an inputted modification policy is made, whether the modified content acquired by the acquisition unit is content in which modification matching the modification policy acquired by the acquisition unit is made”, and “the modification policy comprises a textual input provided by a user while the user provides the content” covers performance of the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind.
MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” The “generation” limitation is an evaluation/judgment of content, and a human is capable of generating modified content based on a modification policy mentally, or with pen and paper. Further, the “evaluation” limitation of claim 9 is an evaluation that can be performed mentally, and, accordingly, the claim recites an abstract idea. Further, the “machine learning model” does not integrate the judicial exception into a practical application because the additional elements are a tangential addition to the claim; see MPEP 2106.05(g).
Considering Step 2A, Prong Two, the judicial exception in claim 9 is not integrated into a practical application. Claim 9 includes the additional elements: “[a] non-transitory computer readable storage medium”, “a computer”, “an acquisition procedure”, and “a machine learning model”. The “medium” and “computer” do not integrate the abstract idea into a practical application because it is mere instructions to implement the abstract idea on a computer; see MPEP 2106.05(f). Further, the “acquisition procedure” amounts to insignificant extra-solution activity, mere data gathering, and does not integrate the claim into a practical application; see MPEP 2106.05(g). Additionally, the “machine learning model” does not integrate the judicial exception into a practical application because the additional elements are a tangential addition to the claim; see MPEP 2106.05(g).
Further, considering Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites additional elements “[a] non-transitory computer readable storage medium”, “a computer”, and “an acquisition procedure” The “medium” and “computer” amount to generic computer components and do not amount to significantly more; see MPEP 2106.2016.05(a) and 2106.2016.05(b). Further, the “acquisition unit” amounts to insignificant extra-solution activity, mere data gathering, and does not amount to significantly more; see MPEP 2106.05(g). Additionally, the machine learning model trained using historical input data is well-understood, routine and conventional. Schreyer, U.S. Patent Application Publication 2017/0161855, discloses that “[t]he rule learning component 244 of the recommendation engine 240 may implement one or more predictive analytics techniques, such as supervised machine learning algorithms, unsupervised machine learning algorithms, and pattern recognition algorithms that are currently well-known in the art to recognize patterns in the historical training data sets 402,” in [0073]. Therefore, the limitation does not amount to significantly more and the claim is not eligible in view of 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-9 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kwatra et al., U.S. Patent Application Publication 2020/0077136 (Kwatra).
With respect to independent claim 1 Kwatra teaches:
An evaluation device comprising (Kwatra teaches a content modification device in at least figures 4-6 that collects user information, evaluates the information to determine how to modify media content, and displays the modified media content.):
a processor (Kwatra teaches a processor in at least [0095].) configured to:
acquire modified content generated by a generation model which generates modified content in which a part of content is modified in a modifying manner based on a modification policy to generate the modified content (Kwatra teaches a machine learning module (generation module) in [0074]-[0076] and that media content modification service takes advantage of machine learning in order to identify personalized suitable timing for any rendered media content and modifies (generates) the visual and audio appeal for such content in [0087].); and
evaluate, by using an evaluation model which evaluates whether inputted content is content in which modification matching an inputted modification policy is made, whether the modified content is content in which modification matching the modification policy is made (Kwatra teaches that a machine learning system may use artificial reasoning to interpret data from one or more sources and learn topics, concepts, and/or processes that may be determined and/or derived by machine learning in [0018]. Kwatra further teaches implementing corrective actions according to monitored behavior of the one or more users, contextual factors, the risk factor, feedback data, detected patterns of discomfort to the heal state, emotional state, or combination thereof of the one or more users in relation to the displayed media content; see [0072]-[0075]. The claim does not detail the modification policy and the various metrics measured by Kwatra used to implement corrective actions can all be considered policies.),
wherein the modification policy comprises a textual input provided by a user while the user provides the content (Kwatra teaches that the discloses system may use a cognitive analysis to generate decisions regarding the particular adjustments (modifications) to the visual and/or audio/sound characteristics of a given portion of media content in according to information explicitly input from the user; see [0022].),
wherein the generation model comprises a machine learning model trained using historical input data and contexts of historical modification policies (Kwatra teaches that user information may include contextual information associated with a current physical location of the user, personal tastes of the user gleaned from previous interactions with historical media content, social media interactions relative to a particular "theme" or product, favorite websites, favorite shows, movies and/or broadcasts, inclinations as to what services and/or products the user 480 uses or intends to user, and the like, in [0078]. [0075] of Kwatra teaches that the machine learning system may learn (be trained) from the data of the user 480.).
With respect to claim 2 the rejection of claim 1 is incorporated. Further, Kwatra teaches:
wherein the processor is further configured to perform learning of the generation model (Kwatra teaches a machine learning module in [0074]-[0076] and that media content modification service takes advantage of machine learning in order to identify personalized suitable timing for any rendered media content and modifies the visual and audio appeal for such content in [0087].).
With respect to claim 3 the rejection of claim 2 is incorporated. Further, Kwatra teaches:
reinforcement learning of the generation model is performed based on an obtained evaluation result (Kwatra teaches the machine learning module may implement a variety of machine learning models, including reinforcement learning, in [0076] and [0087].).
With respect to claim 4 the rejection of claim 1 is incorporated. Further Kwatra teaches:
wherein a model which evaluates whether content is content in which modification matching a context based on the modification policy is made is used as the evaluation model (Kwatra teaches that a machine learning system may use artificial reasoning to interpret data from one or more sources and learn topics, concepts, and/or processes that may be determined and/or derived by machine learning in [0018]. Kwatra further teaches implementing corrective actions according to monitored behavior of the one or more users, contextual factors, the risk factor, feedback data, detected patterns of discomfort to the heal state, emotional state, or combination thereof of the one or more users in relation to the displayed media content; see [0072]-[0075].).
With respect to claim 5 the rejection of claim 1 is incorporated. Further Kwatra teaches:
wherein when the modification policy and the content in which the modification matching the modification policy is made are inputted,
use, as the evaluation model, a model in which learning is performed so as to output higher evaluation than evaluation made when the modification policy and content in which modification not matching the modification policy is made are inputted (Kwatra teaches rendering the color of media content so that the color and one of the rendered advertisement is pronounced when compared to a color and tone of an underlying background or alternative media content in [0058]. The rendering is done via machine learning and corresponding policies; see the rejection of claim 1 above and [0072]-[0075] of Kwatra. The background or alternative media content taught by Kwatra is content that does not match the modification policy and it does not stand out (lower evaluation) than the modified content.).
With respect to claim 6 the rejection of claim 1 is incorporated. Further Kwatra teaches:
wherein when the modification policy and the content are inputted,
acquire the modified content generated by a model, as the generation model, in which learning is performed so as to generate the modified content in which a part of the content is modified in accordance with the context indicated by the modification policy (Kwatra teaches a machine learning module (generation module) in [0074]-[0076] and that media content modification service takes advantage of machine learning in order to identify personalized suitable timing for any rendered media content and modifies (generates) the visual and audio appeal for such content in [0087].).
With respect to claim 7 the rejection of claim 1 is incorporated. Further Kwatra teaches:
wherein when sound and a moving image are inputted, the acquisition unit
acquire the modified content generated by a model, as the generation model, in which learning is performed so as to generate the modified content in which a part of the moving image is modified in accordance with a context indicated by the sound (Kwatra teaches visual and audio/sound characteristic settings including audio/visual content information associated with advertisements may be context dependent in [0017], and that an analysis of the visual and audio/sound characteristics may be used to understand the user and appropriate level of satisfaction. [0022] of Kwatra teaches adjustments to the visual and/or audio/sound characteristics of a portion of the media according to information explicitly input and/or deduced from the user by the computing device within the context of the IoT environment.).
With respect to independent claim 8 Kwatra teaches:
An evaluation method which a computer executes (Kwatra teaches a content modification device in at least figures 4-6 that collects user information, evaluates the information to determine how to modify media content, and displays the modified media content. The device in Kwatra is used to implement a method.), the method comprising:
an acquisition step of acquiring modified content generated by a generation model which generates modified content in which a part of content is modified in a modifying manner based on a modification policy to generate the modified content (Kwatra teaches a machine learning module (generation module) in [0074]-[0076] and that media content modification service takes advantage of machine learning in order to identify personalized suitable timing for any rendered media content and modifies (generates) the visual and audio appeal for such content in [0087].); and
an evaluation step of evaluating whether or not the modified content acquired by the acquisition step is content in which modification matching the modification policy acquired by the acquisition step is made, by using an evaluation model which evaluates whether or not inputted content is content in which modification matching an inputted modification policy is made (Kwatra teaches that a machine learning system may use artificial reasoning to interpret data from one or more sources and learn topics, concepts, and/or processes that may be determined and/or derived by machine learning in [0018]. Kwatra further teaches implementing corrective actions according to monitored behavior of the one or more users, contextual factors, the risk factor, feedback data, detected patterns of discomfort to the heal state, emotional state, or combination thereof of the one or more users in relation to the displayed media content; see [0072]-[0075]. The claim does not detail the modification policy and the various metrics measured by Kwatra used to implement corrective actions can all be considered policies.),
wherein the modification policy comprises a textual input provided by a user while the user provides the content (Kwatra teaches that the discloses system may use a cognitive analysis to generate decisions regarding the particular adjustments (modifications) to the visual and/or audio/sound characteristics of a given portion of media content in according to information explicitly input from the user; see [0022].),
wherein the generation model comprises a machine learning model trained using historical input data and contexts of historical modification policies (Kwatra teaches that user information may include contextual information associated with a current physical location of the user, personal tastes of the user gleaned from previous interactions with historical media content, social media interactions relative to a particular "theme" or product, favorite websites, favorite shows, movies and/or broadcasts, inclinations as to what services and/or products the user 480 uses or intends to user, and the like, in [0078]. [0075] of Kwatra teaches that the machine learning system may learn (be trained) from the data of the user 480.).
With respect to independent claim 9 Kwatra teaches:
A non-transitory computer readable storage medium having stored therein an evaluation program (Kwatra teaches a content modification device in at least figures 4-6 that collects user information, evaluates the information to determine how to modify media content, and displays the modified media content. The device in figure 5 may be implemented on a non-transitory machine-readable storage medium; see [0077].) which causes a computer to execute:
an acquisition procedure of acquiring modified content generated by a generation model which generates modified content in which a part of content is modified in a modifying manner based on a modification policy to generate the modified content (Kwatra teaches a machine learning module (generation module) in [0074]-[0076] and that media content modification service takes advantage of machine learning in order to identify personalized suitable timing for any rendered media content and modifies (generates) the visual and audio appeal for such content in [0087].); and
an evaluation procedure of evaluating whether or not the modified content acquired by the acquisition procedure is content in which modification matching the modification policy acquired by the acquisition procedure is made, by using an evaluation model which evaluates whether or not inputted content is content in which modification matching an inputted modification policy is made (Kwatra teaches that a machine learning system may use artificial reasoning to interpret data from one or more sources and learn topics, concepts, and/or processes that may be determined and/or derived by machine learning in [0018]. Kwatra further teaches implementing corrective actions according to monitored behavior of the one or more users, contextual factors, the risk factor, feedback data, detected patterns of discomfort to the heal state, emotional state, or combination thereof of the one or more users in relation to the displayed media content; see [0072]-[0075]. The claim does not detail the modification policy and the various metrics measured by Kwatra used to implement corrective actions can all be considered policies.),
wherein the modification policy comprises a textual input provided by a user while the user provides the content (Kwatra teaches that the discloses system may use a cognitive analysis to generate decisions regarding the particular adjustments (modifications) to the visual and/or audio/sound characteristics of a given portion of media content in according to information explicitly input from the user; see [0022].),
wherein the generation model comprises a machine learning model trained using historical input data and contexts of historical modification policies (Kwatra teaches that user information may include contextual information associated with a current physical location of the user, personal tastes of the user gleaned from previous interactions with historical media content, social media interactions relative to a particular "theme" or product, favorite websites, favorite shows, movies and/or broadcasts, inclinations as to what services and/or products the user 480 uses or intends to user, and the like, in [0078]. [0075] of Kwatra teaches that the machine learning system may learn (be trained) from the data of the user 480.).
Response to Arguments
Applicant's arguments filed June 4, 2025 have been fully considered but they are not persuasive.
The previous objection to the specification is withdrawn in view of Applicant’s amendment and arguments on page 6 of remarks.
Also, on page 6, the previous rejection of claim 6 under 35 U.S.C. 112(b) is withdrawn in view of Applicant’s amendment.
Beginning on page 6, Applicant argues that the claims are eligible in view of 101. On page 7, Applicant argues that the claims are not directed to a mental process but provides no explanation or citation in support of this argument, and it is not persuasive. Applicant also argues that the claims are eligible because the claims implement a practical application because the claims recite a technical solution to a technical problem. MPEP 2106.05(a) indicates that “[i]f it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification,” and that “[a]n indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art.” Applicant’s arguments do not provide an indication that the claimed invention provides an improvement nor do they show where in the specification a technical problem and explanation of an unconventional solution, as required by the MPEP. Applicant’s arguments are not persuasive.
Beginning on page 8 of remarks, Applicant argues that Kwatra does not teach the claimed features. In particular, Applicant argues that Kwatra does not teach the previously claimed acquisition unit details. Claim 1 recites: “acquire modified content generated by a generation model which generates modified content in which a part of content is modified in a modifying manner based on a modification policy to generate the modified content.” The claim does not detail how or where the modified content is acquired from, what the content is, how it is modified, or provide details as to what the modification policy is. In short, the claim is very broad and is being treated as such. Further, Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. [0074]-[0076], cited above, and previously, disclose more than an application of user reactions, as summarized on page 8 of remarks, and describe at least how the machine learning system is updated over time (i.e. modified) and how the machine learning is implemented; see paragraph [0076], specifically. Applicant’s argument is not persuasive.
Conclusion
Claims 1-9 are rejected.
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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/DANIEL T PELLETT/Primary Examiner, Art Unit 2121