DETAILED ACTION
This office action is responsive to the request for reconsideration filed 1/6/2026. The application contains claims 1-11, 14-22, all examined and rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-22 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “preferred” in claims 1, 17-18, and 20 is a relative term which renders the claim indefinite. The term “preferred” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The “preferred” is a subjective term that cannot be measured. Dependent claim inherit the deficiency of independent claims 1, and 20.
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 1-11, and 14-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
While independent claims 1 and 20 are each directed to a statutory category, it recites a series of steps pertaining to analyze received data to identify features that are used to determine mental state, which appears to be directed to an abstract idea (mental process, mathematical concept).
Claims 1-11, and 14-22 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. 101, (1) 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. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. 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 amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG)
STEP 1.
Per Step 1, the claims are determined to include process and machine as in independent Claim 1 and 20, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category.
At step 2A, prong 1, The invention is directed to identifying features within received video data to determine mental state and identify target mental state which is akin to Mental Process (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are:
“extracting image data and audio data from the video data; extracting semantic text data from the audio data; analyzing at least one of the image data, the audio data, and the semantic text data to identify a first set of features”, “predicting a baseline mental state of the individual based on the first set of features, wherein: the baseline mental state comprises a first mental state value and a second mental state value; the first mental state value corresponds to a first dimension of a multidimensional mental state model; and the second mental state value corresponds to a second dimension of the multidimensional mental state model; identifying a target mental state, wherein: the target mental state comprises a third mental state value and a fourth mental state value; the third mental state value corresponds to the first dimension of the multidimensional mental state model; and the fourth mental state value corresponds to the second dimension of the multidimensional mental state model“, “incrementally, simulating, a plurality of steps forming a predicted path from the baseline mental state toward the target mental state using the multidimensional mental state model, a plurality of actions” (Mental process, observation, evaluation and judgment), “relate actions of the plurality of actions and changes in value in at least one of the first dimension and the second dimension of the multidimensional mental state model” (Mental process, observation, evaluation and judgment, Mathematical concept), “incrementally simulating the plurality of steps of the predicted path comprises: simulating changes to the baseline mental state using the first computer-implemented machine-learning model and the plurality of actions to identify a first step of the predicted path, the first step comprising a first action of the first set of actions predicted to adjust the baseline mental state to a first preferred intermediate mental state; simulating changes to the first preferred intermediate mental state using the plurality of actions to identify a second step of the predicted path, the second step comprising a second action of the first set of actions predicted to adjust the first preferred intermediate mental state toward the target mental state” (Mental process, observation, evaluation and judgment).
The claim recites additional elements as
“acquiring video data of an individual” (insignificant extra-solution activity, MPEP 2106.05(g)); “Simulator”, “computer-implemented machine learning model”, “using the first computer implemented machine learning model” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C)); “predicted path comprises one or more actions of the plurality of actions predicted by the first computer-implemented machine learning model to adjust the baseline mental state to the target mental state via the plurality of steps; each step of the plurality of steps describes a change to at least one of the first dimension and the second dimension of the multidimensional mental state model; and the one or more actions are performable by the individual; and (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use which indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)), “outputting an indication of the one or more actions to the individual” (insignificant extra-solution activity, MPEP 2106.05(g)).
This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract.
STEP 2B.
Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts.
The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s).
When taken the steps individually, these steps are:
“acquiring video data of an individual” (Well-Understood, routine, conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)); “Simulator”, “computer-implemented machine learning model”, “using the first computer implemented machine learning model” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2));
“predicted path comprises one or more actions of the plurality of actions predicted by the first computer-implemented machine learning model to adjust the baseline mental state to the target mental state via the plurality of steps; each step of the plurality of steps describes a change to at least one of the first dimension and the second dimension of the multidimensional mental state model; and the one or more actions are performable by the individual” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use which indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)), “outputting an indication of the one or more actions to the individual” (Well-Understood, routine, conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)).
In the instant case, Claim 1 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves.
Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts.
Further, note that the limitations, in the instant claims, are done by the generically
recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions.
Claim 20 recites a system comprising “processor; a user interface; and a memory encoded with instructions that, when executed, cause the processor” configured to perform the same method as set forth in claim 1, the added element of “processor ;a user interface; and a memory encoded with instructions that, when executed, cause the processor” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is amount to a mere instruction to apply the judicial exception to a computer (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)).
Claim 20 is therefore rejected according to the same findings and rationale as provided above.
CONCLUSION
It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish).
The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim.
claims 2 disclose target mental state is identified based on a task performed by the individual (Mental process); and the video data depicts the individual performing the task (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use which indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). Claim 2 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. claims 3 disclose wherein predicting the baseline mental state comprises: generating, by a second computer-implemented machine learning model, the first mental state value based on the first set of features; and generating, by a third computer-implemented machine learning model, the second mental state value based on the first set of features (determining mental state value based on features is a mental process and computer-implemented machine learning model indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). Claim 3 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. claims 4 disclose wherein the first dimension describes an intensity of a first mental state and the second dimension describes a pleasantness of the first mental state (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use which indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). Claim 4 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 5 disclose wherein the first dimension describes an intensity of the first mental state, a pleasantness of the first mental state, an importance of information conveyed by the individual, a positivity of the conveyed information, or a subject of the conveyed information (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use which indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). Claim 5 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 6 disclose wherein the first dimension describes a first mental state, the second dimension describes a second mental state, and the first mental state and the second mental state are selected from a group consisting of tiredness, sleepiness, serenity, satisfaction, calmness, relaxation, contentment, distress, frustration, anger, annoyance, tension, fear, alarm, misery, sadness, depression, gloom, boredom, astonishment, amusement, excitement, happiness, delight, gladness, pleasure, thankfulness, gratitude, confusion, smugness, deliberation, anticipation, cheer, sympathy, trust, humor, envy, melancholy, hostility, resentment, revulsion, and ennui (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use which indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). Claim 6 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 7 disclose analyzing at least one of the image data, the audio data, and the semantic text data to identify the first set of features comprises: analyzing the image data to identify the first set of features; analyzing the audio data to identify a second set of features; and analyzing the semantic text data to identify a third set of features (Mental Process). Claim 7 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 8 disclose herein predicting the baseline mental state comprises: generating, by a second computer-implemented machine learning model, the first mental state value based on at least one of the first set of features, the second set of features, and the third set of features; and generating, by a third computer-implemented machine learning model, the second mental state value based on at least one of the first set of features, the second set of features, and the third set of features (determining mental state value based on features is a mental process and computer-implemented machine learning model indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). Claim 8 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 9 disclose wherein predicting the baseline mental state comprises: generating, by a second computer-implemented machine learning model, the first mental state value based on the first set of features and the second set of features; and generating, by a third computer-implemented machine learning model, the second mental state value based on the first set of features and the second set of features (determining mental state value based on features is a mental process and computer-implemented machine learning model indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). Claim 9 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 10 disclose the baseline mental state comprises a fifth mental state value corresponding to a third dimension of the multidimensional mental state model; the target mental state comprises a sixth mental state value corresponding to the third dimension of the multidimensional mental state model (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use which indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)); the first computer-implemented machine learning model (indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)) is configured to relate actions of the plurality of actions and changes in value in at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model (Mental process); and the predicted path comprises one or more actions of the plurality of actions and corresponding changes to at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use which indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). Claim 10 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 11 disclose wherein predicting the baseline mental state further comprises generating, by a fourth computer-implemented machine learning model, the fifth mental state value based on the third set of features (determining mental state value based on features is a mental process and computer-implemented machine learning model indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). Claim 11 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 14 disclose wherein analyzing the image data to identify the first set of features comprises analyzing the image data with a computer vision model (analyzing image data to identify set of features is a mental process and a computer vision model indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). Claim 14 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 15 disclose analyzing the audio data to identify the second set of features comprises: converting the audio data to a spectrogram (Mental process, Mathematical concept); and analyzing the spectrogram (Mental process, Mathematical concept) with a fourth computer-implemented machine learning model (indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). Claim 15 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 16 disclose wherein analyzing the semantic text data to identify the third set of features comprises analyzing the semantic text data (mental process) with a natural language understanding model (indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). Claim 16 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 17 disclose generating a first plurality of intermediate points based on the changes in value in at least one of the first dimension and the second dimension and the baseline mental state (Mental process), wherein each intermediate point of the first plurality of intermediate points corresponds to an action of the one or more actions (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use which indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)); measuring a first plurality of Euclidean distances between the first plurality of intermediate points and the target mental state (Mental Process, Mathematical concept); determining a first preferred intermediate point of the first plurality of intermediate points, the first preferred intermediate point having a shortest Euclidean distance of the first plurality of Euclidean distances to the target mental state (Mental Process, Mathematical concept); and storing, as a first step of the predicted path, the change in value in at least one of the first dimension and the second dimension used to generate the first preferred intermediate point and the corresponding action of the one or more actions (insignificant extra-solution activity, MPEP 2106.05(g) that is Well-Understood, routine, conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)). Claim 17 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 18 disclose wherein simulating the predicted path further comprises: generating a second plurality of intermediate points based on the changes in value in at least one of the first dimension and the second dimension and the first preferred intermediate point (mental process), wherein each intermediate point of the second plurality of intermediate points corresponds to an action of the one or more actions (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use which indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)); measuring a second plurality of Euclidean distances between the second plurality of intermediate points and the target mental state (mental process, mathematical concept); determining a second preferred intermediate point of the second plurality of intermediate points, the second preferred intermediate point having a shortest Euclidean distance of the second plurality of Euclidean distances to the target mental state (mental process, Mathematical process); and storing, as a second step of the predicted path, the change in value in at least one of the first dimension and the second dimension used to generate the second preferred intermediate point and the corresponding action of the one or more actions (insignificant extra-solution activity, MPEP 2106.05(g) that is Well-Understood, routine, conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)). Claim 18 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 19 disclose outputting the indication of the one or more actions to the individual comprises: cross-referencing, with a table of actions and instructions, the one or more actions to determine one or more instructions for performing the one or more actions (Mental process); and outputting the one or more instructions to the individual (insignificant extra-solution activity, MPEP 2106.05(g) that is Well-Understood, routine, conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)). Claim 19 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 21 disclose “The method of claim 1, wherein:
the baseline mental state comprises a fifth mental state value; the target mental state comprises a sixth mental state value; the fifth and sixth mental state values correspond to a third dimension of the multidimensional mental state model (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use which indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)); the first computer-implemented machine learning model (a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h))) is configured to relate actions of the plurality of actions and changes in value in at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model (Mental process); and each step of the plurality of steps describes a change to at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use which indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). Claim 21 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. Claim 22 disclose “The method of claim 21, wherein: the baseline mental state comprises a seventh mental state value; the target mental state comprises an eighth mental state value; the seventh and eighth mental state values correspond to a fourth dimension of the multidimensional mental state model (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use which indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)); the first computer-implemented machine learning model is configured to (a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h))) relate actions of the plurality of actions and changes in value in at least one of the first dimension, the second dimension, the third dimension, and the fourth dimension of the multidimensional mental state model (mental process); and each step of the plurality of steps describes a change to at least one of the first dimension, the second dimension, the third dimension, and the fourth dimension of the multidimensional mental state model” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use which indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). Claim 22 does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea.
The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1 ; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed. For at least these reasons, the claimed inventions of each of dependent claims 2-11, 14-19, 21-22,are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-11, 14-22 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12299490 in view of US 20230113072 A1 [hereinafter D1]. Although the claims at issue are not identical, they are not patentably distinct from each other because they are claiming the same exact concepts.
Current Application
U.S. Patent No. 12299490
D1
Claim 1
A method comprising: acquiring video data of an individual;
extracting image data and audio data from the video data;
extracting semantic text data from the audio data; analyzing at least one of the image data, the audio data, and the semantic text data to identify a first set of features;
predicting a baseline mental state of the individual based on the first set of features, wherein: the baseline mental state comprises a first mental state value and a second mental state value;
the first mental state value corresponds to a first dimension of a multidimensional mental state model; and
the second mental state value corresponds to a second dimension of the multidimensional mental state model;
identifying a target mental state, wherein: the target mental state comprises a third mental state value and a fourth mental state value;
the third mental state value corresponds to the first dimension of the multidimensional mental state model; and the fourth mental state value corresponds to the second dimension of the multidimensional mental state model;
incrementally, simulating, by a simulator, a plurality of steps forming a predicted path from the baseline mental state toward the target mental state using the multidimensional mental state model, a plurality of actions, and a first computer-implemented machine learning model, wherein:
the first computer-implemented machine learning model is configured to relate actions of the plurality of actions and changes in value in at least one of the first dimension and the second dimension of the multidimensional mental state model;
the predicted path comprises one or more actions of the plurality of actions predicted by the first computer-implemented machine learning model to adjust the baseline mental state to the target mental state via the plurality of steps; each step of the plurality of steps describes a change to at least one of the first dimension and the second dimension of the multidimensional mental state model; and the one or more actions are performable by the individual; and
incrementally simulating the plurality of steps of the predicted path comprises:simulating changes to the baseline mental state using the first computer-implemented machine-learning model and the plurality of actions to identify a first step of the predicted path, the first step comprising a first action of the first set of actions predicted to adjust the baseline mental state to a first preferred intermediate mental state;simulating changes to the first preferred intermediate mental state using the first computer-implemented machine-learning model and the plurality of actions to identify a second step of the predicted path, the second step comprising a second action of the first set of actions predicted to adjust the first preferred intermediate mental state toward the target mental state; and
outputting an indication of the one or more actions to the individual.
Claim 1
A method comprising:
acquiring video data of a first individual;
extracting image data and audio data from the video data;
extracting semantic text data from the audio data;
analyzing at least one of the image data, the audio data, and the semantic text data to identify a first set of features;
predicting a baseline mental state of the first individual based on the first set of features, wherein: the baseline mental state comprises a first mental state value and a second mental state value;
the first mental state value corresponds to a first dimension of a multidimensional mental state model; and
the second mental state value corresponds to a second dimension of the multidimensional mental state model;
identifying a target mental state for the first individual, wherein:
the target mental state comprises a third mental state value and a fourth mental state value;
the third mental state value corresponds to the first dimension of the multidimensional mental state model; and
the fourth mental state value corresponds to the second dimension of the multidimensional mental state model;
simulating, by a simulator, a predicted path from the baseline mental state toward the target mental state using the multidimensional mental state model, a plurality of actions, and a first computer-implemented machine learning model, wherein:
the first computer-implemented machine learning model is configured to relate actions of the plurality of actions and changes in value in at least one of the first dimension and the second dimension of the multidimensional mental state model;
the predicted path comprises one or more actions of the plurality of actions and corresponding changes to at least one of the first dimension and the second dimension of the multidimensional mental state model; and the one or more actions are performable by a second individual to adjust the baseline mental state of the first individual toward the target mental state; and
outputting an indication of the one or more actions to the second individual
U.S. Patent No. 12299490 does not disclose that the the first computer-implemented machine learning model to adjust the baseline mental state to the target mental state via the plurality of steps; each step of the plurality of steps describes a change; incrementally simulating the plurality of steps of the predicted path comprises:
simulating changes to the baseline mental state using the first computer-implemented machine-learning model and the plurality of actions to identify a first step of the predicted path, the first step comprising a first action of the first set of actions predicted to adjust the baseline mental state to a first preferred intermediate mental state; simulating changes to the first preferred intermediate mental state using the first computer-implemented machine-learning model and the plurality of actions to identify a second step of the predicted path, the second step comprising a second action of the first set of actions predicted to adjust the first preferred intermediate mental state toward the target mental state; and
D1 disclose that the actions are performable by the individual (¶117, user listen to the provided audio segment); and
i incrementally simulating, by a simulator, a plurality of steps forming a predicted path from the baseline mental state toward the target mental state using the multidimensional mental state model (¶8, “audio stream … is generated with the intent of effecting a controlled trajectory of the listener's affective state from the current state to the target state”, ¶9, “identifying an affective trajectory from the current affective state to the target affective state”, ¶116, “current affective state 212 of the listener is plotted as a starting point 252 for the curve 250. The target affective state 214 is plotted as an endpoint 254 of the curve 250. One or more intermediate waypoints may be plotted along the curve 250, such as first waypoint 256 and second waypoint 258, indicating intermediate affective states on the affective trajectory 218. An initial portion 260 of the curve 250 is defined by the starting point 252 and first waypoint 256. A second subsequent portion 262 of the curve 250 is defined by the first waypoint 256 and the second waypoint 258. A third and final subsequent portion 264 of the curve 250 is defined by the second waypoint 258 and the endpoint 254. Machine learning techniques can also be implemented to learn the best trajectory for individuals using the system, making these trajectories dynamic based on previous success at achieving the user's target affective state”, ¶101, “two separate machine learning models to generate audio … One machine learning model is an affective inference model … other machine learning system is a reinforcement learning model with a deep learning neural network—also called a Deep Q Network (DQN)”) a plurality of actions (¶117, “audio segment identification process 222 is used to select or identify an audio segment … from a subset of the audio segments 220 stored in the audio library”, the plurality of actions are listening to any of the different possible songs that could be provided to the user to listen); the predicted path comprises one or more actions of the plurality of actions predicted by the first computer-implemented machine learning model to adjust the baseline mental state to the target mental state via the plurality of steps (¶8, “audio stream is generated by a machine learning model t… indicating the effectiveness of specific audio segments, or audio segments having specific features, in effecting the desired affective trajectory”, ¶9, “machine learning model to identify a first audio segment likely to induce in the listener a desired affective response corresponding to at least an initial portion of the affective trajectory”, ¶11, “machine learning model is used to identify a subsequent audio segment likely to induce in the listener a subsequent desired affective response corresponding to at least an initial portion of the updated affective trajectory when the subsequent audio segment is presented to the listener as an auditory stimulus. The audio stream is generated based at least in part on the first audio segment and the subsequent audio segment”);
each step of the plurality of steps describes a change to at least one of the first dimension and the second dimension of the multidimensional mental state model (Fig. 2A-2B, ¶11, “infer an inferred new affective state based on the current affective state and a set of audio feature values of the first audio segment. An updated affective trajectory from the inferred new affective state data to the target affective state is identified. The trained segment identification machine learning model is used to identify a subsequent audio segment “, ¶160, “updated state valence (the valence value of the inferred new affective state 226 or updated current affective state 212 prior to the current step), and updated state activation (the activation value of the inferred new affective state 226 or updated current affective state 212 prior to the current step)“, ¶117, “audio segment 230 is selected based on an assessment by the DQN 120 that the audio segment 230 is more likely than other audios segments in the subset of the audio segments 220 to induce at least the initial portion 260 of the affective trajectory 218 in the listener, i.e., that the audio segment 230, when played to the listener as an auditory stimulus, is likely to induce an affective state in the listener close to the state represented by the first waypoint 256 or one of the subsequent points 258, 254 on the affective trajectory”, ¶127); the one or more actions are performable by the individual (¶117, user listen to the provided audio segment); incrementally simulating the plurality of steps of the predicted path comprises:
simulating changes to the baseline mental state using the first computer implemented machine-learning model and the plurality of actions to identify a first step of the predicted path (Fig. 2A-2B, ¶11, “identify a first audio segment likely to induce in the listener a desired affective response corresponding to at least an initial portion of the affective trajectory in the listener when presented to the listener as an auditory stimulus”, ¶116, “starting point 252 for the curve 250 … first waypoint 256 … initial portion 260“), the first step comprising a first action of the first set of actions predicted to adjust the baseline mental state to a first preferred intermediate mental state (¶117, “…the audio segment 230, when played to the listener as an auditory stimulus, is likely to induce an affective state in the listener close to the state represented by the first waypoint 256 or one of the subsequent points 258, 254 on the affective trajectory”); simulating changes to the first preferred intermediate mental state using the first computer-implemented machine-learning model and the plurality of actions to identify a second step of the predicted path (¶11, “ An updated affective trajectory from the inferred new affective state data to the target affective state is identified. The trained segment identification machine learning model is used to identify a subsequent audio segment“), the second step comprising a second action of the first set of actions predicted to adjust the first preferred intermediate mental state toward the target mental state (¶11, “trained segment identification machine learning model is used to identify a subsequent audio segment likely to induce in the listener a subsequent desired affective response corresponding to at least an initial portion of the updated affective trajectory …”, ¶116, “second subsequent portion 262 of the curve 250 is defined by the first waypoint 256 and the second waypoint 258. A third and final subsequent portion 264 of the curve 250 is defined by the second waypoint 258 and the endpoint 254”); and outputting an indication of the one or more actions to the individual (¶117, providing audio segment to the user to listen). U.S. Patent No. 12299490 and D1 are analogous art to the claimed invention because they are from a similar field of endeavor of identifying and controlling emotional state. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify U.S. Patent No. 12299490 resulting in resolutions as disclosed by D1 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify U.S. Patent No. 12299490 as described above to provide and recommend music intended to induce specific changes in a listener's affective state (D1, ¶1).
Claim 2
The method of claim 1, wherein:
the target mental state is identified based on a task performed by the individual; and
the video data depicts the individual performing the task.
Claim 2
The method of claim 1, wherein:
the target mental state is identified based on a task performed by the first individual; and
the video data depicts the first individual performing the task.
Claim 3
the method of claim 1, wherein predicting the baseline mental state comprises:
generating, by a second computer-implemented machine learning model, the first mental state value based on the first set of features; and
generating, by a third computer-implemented machine learning model, the second mental state value based on the first set of features.
Claim 5
The method of claim 1, wherein predicting the baseline mental state comprises:
generating, by a second computer-implemented machine learning model, the first mental state value based on the first set of features; and
generating, by a third computer-implemented machine learning model, the second mental state value based on the first set of features.
Claim 4
he method of claim 3, wherein the first dimension describes an intensity of a first mental state and the second dimension describes a pleasantness of the first mental state.
Claim 6
The method of claim 5, wherein the first dimension describes an intensity of a first mental state and the second dimension describes a pleasantness of the first mental state.
Claim 5
The method of claim 3, wherein the first dimension describes an intensity of the first mental state, a pleasantness of the first mental state, an importance of information conveyed by the individual, a positivity of the conveyed information, or a subject of the conveyed information.
Claim 7
The method of claim 5, wherein the first dimension describes an intensity of the first mental state, a pleasantness of the first mental state, an importance of information conveyed by the first individual, a positivity of the conveyed information, or a subject of the conveyed information.
Claim 6
The method of claim 3, wherein the first dimension describes a first mental state, the second dimension describes a second mental state, and the first mental state and the second mental state are selected from a group consisting of tiredness, sleepiness, serenity, satisfaction, calmness, relaxation, contentment, distress, frustration, anger, annoyance, tension, fear, alarm, misery, sadness, depression, gloom, boredom, astonishment, amusement, excitement, happiness, delight, gladness, pleasure, thankfulness, gratitude, confusion, smugness, deliberation, anticipation, cheer, sympathy, trust, humor, envy, melancholy, hostility, resentment, revulsion, and ennui.
Claim 8
The method of claim 5, wherein the first dimension describes a first mental state, the second dimension describes a second mental state, and the first mental state and the second mental state are selected from a group consisting of tiredness, sleepiness, serenity, satisfaction, calmness, relaxation, contentment, distress, frustration, anger, annoyance, tension, fear, alarm, misery, sadness, depression, gloom, boredom, astonishment, amusement, excitement, happiness, delight, gladness, pleasure, thankfulness, gratitude, confusion, smugness, deliberation, anticipation, cheer, sympathy, trust, humor, envy, melancholy, hostility, resentment, revulsion, and ennui.
Claim 7
The method of claim 1, wherein analyzing at least one of the image data, the audio data, and the semantic text data to identify the first set of features comprises:
analyzing the image data to identify the first set of features;
analyzing the audio data to identify a second set of features; and
analyzing the semantic text data to identify a third set of features.
Claim 9
The method of claim 1, wherein analyzing at least one of the image data, the audio data, and the semantic text data to identify the first set of features comprises:
analyzing the image data to identify the first set of features;
analyzing the to identify a second set of features; and
analyzing the semantic text data to identify a third set of features.
Claim 8
The method of claim 7, wherein predicting the baseline mental state comprises:
generating, by a second computer-implemented machine learning model, the first mental state value based on at least one of the first set of features, the second set of features, and the third set of features; and
generating, by a third computer-implemented machine learning model, the second mental state value based on at least one of the first set of features, the second set of features, and the third set of features.
Claim 10
The method of claim 9, wherein predicting the baseline mental state comprises:
generating, by a second computer-implemented machine learning model, the first mental state value based on at least one of the first set of features, the second set of features, and the third set of features; and
generating, by a third computer-implemented machine learning model, the second mental state value based on at least one of the first set of features, the second set of features, and the third set of features.
Claim 9
The method of claim 7, wherein predicting the baseline mental state comprises:
generating, by a second computer-implemented machine learning model, the first mental state value based on the first set of features and the second set of features; and
generating, by a third computer-implemented machine learning model, the second mental state value based on the first set of features and the second set of features.
Claim 11
The method of claim 9, wherein predicting the baseline mental state comprises:
generating, by a second computer-implemented machine learning model, the first mental state value based on the first set of features and the second set of features; and
generating, by a third computer-implemented machine learning model, the second mental state value based on the first set of features and the second set of features.
Claim 10
The method of claim 9, wherein:
the baseline mental state comprises a fifth mental state value corresponding to a third dimension of the multidimensional mental state model;
the target mental state comprises a sixth mental state value corresponding to the third dimension of the multidimensional mental state model;
the first computer-implemented machine learning model is configured to relate actions of the plurality of actions and changes in value in at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model; and
the predicted path comprises one or more actions of the plurality of actions and corresponding changes to at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model.
Claim 12
The method of claim 11, wherein:
the baseline mental state comprises a fifth mental state value corresponding to a third dimension of the multidimensional mental state model;
the target mental state comprises a sixth mental state value corresponding to the third dimension of the multidimensional mental state model;
the first computer-implemented machine learning model is configured to relate actions of the plurality of actions and changes in value in at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model; and
the predicted path comprises one or more actions of the plurality of actions and corresponding changes to at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model.
Claim 11
The method of claim 10, wherein predicting the baseline mental state further comprises generating, by a fourth computer-implemented machine learning model, the fifth mental state value based on the third set of features.
Claim 13
The method of claim 12, wherein predicting the baseline mental state further comprises generating, by a fourth computer-implemented machine learning model, the fifth mental state value based on the third set of features.
Claim 14
The method of claim 7, wherein analyzing the image data to identify the first set of features comprises analyzing the image data with a computer vision model.
D1, ¶172, “Camera data may be used to analyze facial expressions or other behavioral patterns correlated with affective state or affective response”
The same motivation to combine for claim 1 equally applies for current claim
Claim 15
The method of claim 7, wherein analyzing the audio data to identify the second set of features comprises:
converting the audio data to a spectrogram; and
analyzing the spectrogram with a fourth computer-implemented machine learning model.
Claim 16
The method of claim 9, wherein analyzing the audio data to identify the second set of features comprises:
converting the audio data to a spectrogram; and
analyzing the spectrogram with a fourth computer-implemented machine learning model.
Claim 16
The method of claim 7, wherein analyzing the semantic text data to identify the third set of features comprises analyzing the semantic text data with a natural language understanding model.
Claim 1
extracting semantic text data from the audio data;
analyzing at least one of the image data, the audio data, and the semantic text data to identify a first set of features
¶172, “Speech recordings or transcriptions may reveal patterns of prosody, intonation, or speech content correlated with affective state or affective response. In some embodiments, the listener device 190 or another process internal or external to the system 100 may be used to collect and/or process camera, speech, or other user data to assist in identifying a listener's current affective state”.
The same motivation to combine for claim 1 equally applies for current claim
Claim 17
The method of claim 1, wherein the simulating the predicted path comprises:
generating a first plurality of intermediate points based on the changes in value in at least one of the first dimension and the second dimension and the baseline mental state, wherein each intermediate point of the first plurality of intermediate points corresponds to an action of the one or more actions;
measuring a first plurality of Euclidean distances between the first plurality of intermediate points and the target mental state;
determining a first preferred intermediate point of the first plurality of intermediate points, the first preferred intermediate point having a shortest Euclidean distance of the first plurality of Euclidean distances to the target mental state; and
storing, as a first step of the predicted path, the change in value in at least one of the first dimension and the second dimension used to generate the first preferred intermediate point and the corresponding action of the one or more actions.
Claim 17
The method of claim 1, wherein the simulating the predicted path comprises:
generating a first plurality of intermediate points based on the changes in value in at least one of the first dimension and the second dimension and the baseline mental state, wherein each intermediate point of the first plurality of intermediate points corresponds to an action of the one or more actions;
measuring a first plurality of Euclidean distances between the first plurality of intermediate points and the target mental state;
determining a first preferred intermediate point of the first plurality of intermediate points, the first preferred intermediate point having a shortest Euclidean distance of the first plurality of Euclidean distances to the target mental state; and
storing, as a first step of the predicted path, the change in value in at least one of the first dimension and the second dimension used to generate the first preferred intermediate point and the corresponding action of the one or more actions.
Claim 18
The method of claim 17, wherein simulating the predicted path further comprises:
generating a second plurality of intermediate points based on the changes in value in at least one of the first dimension and the second dimension and the first preferred intermediate point, wherein each intermediate point of the second plurality of intermediate points corresponds to an action of the one or more actions;
measuring a second plurality of Euclidean distances between the second plurality of intermediate points and the target mental state;
determining a second preferred intermediate point of the second plurality of intermediate points, the second preferred intermediate point having a shortest Euclidean distance of the second plurality of Euclidean distances to the target mental state; and
storing, as a second step of the predicted path, the change in value in at least one of the first dimension and the second dimension used to generate the second preferred intermediate point and the corresponding action of the one or more actions.
Claim 18
The method of claim 17, wherein simulating the predicted path further comprises:
generating a second plurality of intermediate points based on the changes in value in at least one of the first dimension and the second dimension and the first preferred intermediate point, wherein each intermediate point of the second plurality of intermediate points corresponds to an action of the one or more actions;
measuring a second plurality of Euclidean distances between the second plurality of intermediate points and the target mental state;
determining a second preferred intermediate point of the second plurality of intermediate points, the second preferred intermediate point having a shortest Euclidean distance of the second plurality of Euclidean distances to the target mental state; and
storing, as a second step of the predicted path, the change in value in at least one of the first dimension and the second dimension used to generate the second preferred intermediate point and the corresponding action of the one or more actions.
Claim 19
The method of claim 1, wherein outputting the indication of the one or more actions to the individual comprises:
cross-referencing, with a table of actions and instructions, the one or more actions to determine one or more instructions for performing the one or more actions; and
outputting the one or more instructions to the individual.
Claim 19
The method of claim 1, wherein outputting the indication of the one or more actions to the second individual comprises:
cross-referencing, with a table of actions and instructions, the one or more actions to determine one or more instructions for performing the one or more actions; and
outputting the one or more instructions to the second individual.
cross-referencing, with a table of actions and instructions, the one or more actions to determine one or more instructions for performing the one or more actions (D1, ¶117, “audio segment identification process 222 is used to select or identify an audio segment … from a subset of the audio segments 220 stored in the audio library”, the audio library is a table that store set of audio segments, each represent an instructions for the action of listening to the song); and outputting the one or more instructions to the individual (D1, ¶117, “audio segment identification process 222 is used to select or identify an audio segment … from a subset of the audio segments 220 stored in the audio library”, providing the selected audio segment to the user where the audio segment is the instruction for performing an action of listening to the selected audio segment).
The same motivation to combine for claim 1 equally applies for current claim
Claim 21
The method of claim 1, wherein: the baseline mental state comprises a fifth mental state value; the target mental state comprises a sixth mental state value; the fifth and sixth mental state values correspond to a third dimension of the multidimensional mental state model; the first computer-implemented machine learning model is configured to relate actions of the plurality of actions and changes in value in at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model; and each step of the plurality of steps describes a change to at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model.
Claim 12
The method of claim 11, wherein:
the baseline mental state comprises a fifth mental state value corresponding to a third dimension of the multidimensional mental state model;
the target mental state comprises a sixth mental state value corresponding to the third dimension of the multidimensional mental state model;
the first computer-implemented machine learning model is configured to relate actions of the plurality of actions and changes in value in at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model; and
the predicted path comprises one or more actions of the plurality of actions and corresponding changes to at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model.
Fig. 2A-2B, ¶11, “infer an inferred new affective state based on the current affective state and a set of audio feature values of the first audio segment. An updated affective trajectory from the inferred new affective state data to the target affective state is identified. The trained segment identification machine learning model is used to identify a subsequent audio segment “, ¶160, ¶117
The same motivation to combine for claim 1 equally applies for current claim
Claim 22
The method of claim 21, wherein: the baseline mental state comprises a seventh mental state value; the target mental state comprises an eighth mental state value; the seventh and eighth mental state values correspond to a fourth dimension of the multidimensional mental state model; the first computer-implemented machine learning model is configured to relate actions of the plurality of actions and changes in value in at least one of the first dimension, the second dimension, the third dimension, and the fourth dimension of the multidimensional mental state model; and each step of the plurality of steps describes a change to at least one of the first dimension, the second dimension, the third dimension, and the fourth dimension of the multidimensional mental state model.
the baseline mental state comprises a seventh mental state value (D1, ¶113, “ Additional biomarkers inferred from physiological data can also be used as inputs to the current state identification process, even beyond the two-dimensional valence and activation values, such as anxiety level, focus levels, agitation levels, etc.”);
the target mental state comprises an eighth mental state value (D1, ¶111, “… some embodiments may use other affect models, including models that use more or fewer than two dimensions to characterize affective states …”, ¶115, “A target state identification process 204 is used to identify the listener's target affective state 214”);
the seventh and eighth mental state values correspond to a fourth dimension of the multidimensional mental state model (D1, ¶111, “… some embodiments may use other affect models, including models that use more or fewer than two dimensions to characterize affective states …”, ¶113, “beyond the two-dimensional valence and activation values, such as anxiety level, focus levels, agitation levels, etc.”);
the first computer-implemented machine learning model is configured to relate actions of the plurality of actions and changes in value in at least one of the first dimension, the second dimension, the third dimension, and the fourth dimension of the multidimensional mental state model (D1, ¶117, “An audio segment identification process 222 is used to select or identify an audio segment that, when presented to the listener as an auditory stimulus, is likely to induce at least the initial portion 260 of the affective trajectory 218 in the affective state of the listener”, ¶11, “an affective inference process is used to infer an inferred new affective state based on the current affective state and a set of audio feature values of the first audio segment. An updated affective trajectory from the inferred new affective state data to the target affective state is identified”, ¶137, “the affect inference process 224 uses the trained affect inference machine learning model 140 to predict how the audio segment selected by the deep learning neural network 120 at step 308 will affect the user. This inferred new affective state data 226 is generated by the affect inference machine learning model 140 at step 310 and sent to the DQN 120 as a state data input”); and
each step of the plurality of steps describes a change to at least one of the first dimension, the second dimension, the third dimension, and the fourth dimension of the multidimensional mental state model (D1, ¶11, “an affective inference process is used to infer an inferred new affective state based on the current affective state and a set of audio feature values of the first audio segment. An updated affective trajectory from the inferred new affective state data to the target affective state is identified”, ¶115, ¶136, “At step 308, a trained segment identification machine learning model (e.g. DQN 120) is used to identify a first audio segment (e.g. audios segment 230) likely to induce in the listener a desired affective response corresponding to at least an initial portion (e.g. initial portion 256) of the affective trajectory”, ¶137, “inferred new affective state data 226 is generated by the affect inference machine learning model 140 at step 310 and sent to the DQN 120 as a state data input”, ¶138, “If the method 300 determines at step 316 that the final step has not been reached, subsequent audio segments likely to induce subsequent portions of the affective trajectory are identified”). The same motivation to combine for claim 1 equally applies for current claim.
Claim 20 is similar in scope to claim 1; therefore it is rejected under similar rationale.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-11, 14, 16, and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over Tao et al. [US 2022/0270636 A1, hereinafter Tao] in view of Labbe [US 2023/0113072 A1, hereinafter D1]
With regard to Claim 1,
Tao disclose a method comprising:
acquiring video data of an individual, extracting image data and audio data from the video data, extracting semantic text data from the audio data (Abstract, “extracting acoustic features, text features, and image features from a video file to fuse them into multi-modal features”);
analyzing at least one of the image data (¶55), the audio data (¶53), and the semantic text data (¶54) to identify a first set of features (¶51, “The feature extraction step 1 is configured to extract acoustic features, text features, and image features in a video file”).
Tao does not explicitly disclose predicting a baseline mental state of the individual based on the first set of features, wherein: the baseline mental state comprises a first mental state value and a second mental state value; the first mental state value corresponds to a first dimension of a multidimensional mental state model; and the second mental state value corresponds to a second dimension of the multidimensional mental state model; identifying a target mental state, wherein: the target mental state comprises a third mental state value and a fourth mental state value; the third mental state value corresponds to the first dimension of the multidimensional mental state model; and the fourth mental state value corresponds to the second dimension of the multidimensional mental state model; incrementally simulating, by a simulator, a plurality of steps forming a predicted path from the baseline mental state toward the target mental state using the multidimensional mental state model, a plurality of actions, and a first computer-implemented machine learning model, wherein: the first computer-implemented machine learning model is configured to relate actions of the plurality of actions and changes in value in at least one of the first dimension and the second dimension of the multidimensional mental state model; the predicted path comprises one or more actions of the plurality of actions predicted by the first computer-implemented machine learning model to adjust the baseline mental state to the target mental state via the plurality of steps; changes each step of the plurality of steps describes a change to at least one of the first dimension and the second dimension of the multidimensional mental state model; the one or more actions are performable by the individual; incrementally simulating the plurality of steps of the predicted path comprises: simulating changes to the baseline mental state using the first computer-implemented machine-learning model and the plurality of actions to identify a first step of the predicted path, the first step comprising a first action of the first set of actions predicted to adjust the baseline mental state to a first preferred intermediate mental state; simulating changes to the first preferred intermediate mental state using the first computer-implemented machine-learning model and the plurality of actions to identify a second step of the predicted path, the second step comprising a second action of the first set of actions predicted to adjust the first preferred intermediate mental state toward the target mental state; and outputting an indication of the one or more actions to the individual.
D1 disclose predicting a baseline mental state of the individual based on the first set of features (¶111, “listener state data used to determine a listener's affective state may include camera data showing the listener's facial expressions or behavior, voice data indicating the listener's intonation or speech content”), wherein:
the baseline mental state comprises a first mental state value and a second mental state value (¶112, “two-dimensional model of affect, sometimes called the circumplex model, where a given affective state is represented as a valence value (representing the degree of positive or negative emotion) and an arousal or activation value (representing the degree of emotional alertness or energy). In a two-dimensional valence-activation model of affect, for example, sadness might be represented by a negative valence and low activation … including models that use more or fewer than two dimensions to characterize affective states, models that use time-varying affective values to model affective states, and models that use a list of discrete affective states without using numerical values”, ¶113, “affect identification engine, such as a further machine learning model trained to identify affective states in a specific listener or in humans generally, to identify the listener's affective state based on the listener state data. Additional biomarkers inferred from physiological data can also be used as inputs to the current state identification process, even beyond the two-dimensional valence and activation values, such as anxiety level, focus levels, agitation levels”);
the first mental state value corresponds to a first dimension of a multidimensional mental state model, and the second mental state value corresponds to a second dimension of the multidimensional mental state model (¶¶112-113, “affect identification engine, such as a further machine learning model trained to identify affective states in a specific listener or in humans generally, to identify the listener's affective state based on the listener state data. Additional biomarkers inferred from physiological data can also be used as inputs to the current state identification process, even beyond the two-dimensional valence and activation values, such as anxiety level, focus levels, agitation levels”);
identifying a target mental state, wherein:
the target mental state comprises a third mental state value and a fourth mental state value (¶115, “target affective state data 210 may be predetermined by the nature of the intended application: for example, a relaxation application may always provide target affective state data 210 indicating a low-activation, positive-valence state, whereas a concentration application may provide target affective state data 210 indicating a high-activation, positive-to-neutral valence state. Other embodiments may identify the listener's target affective state 214 based on listener preference data received from the listener device 190 before or during a user session”;
the third mental state value corresponds to the first dimension of the multidimensional mental state model; and the fourth mental state value corresponds to the second dimension of the multidimensional mental state model (¶115, “target affective state data 210 may be predetermined by the nature of the intended application: for example, a relaxation application may always provide target affective state data 210 indicating a low-activation, positive-valence state, whereas a concentration application may provide target affective state data 210 indicating a high-activation, positive-to-neutral valence state. Other embodiments may identify the listener's target affective state 214 based on listener preference data received from the listener device 190 before or during a user session”;
incrementally simulating, by a simulator, a plurality of steps forming a predicted path from the baseline mental state toward the target mental state using the multidimensional mental state model (¶8, “audio stream … is generated with the intent of effecting a controlled trajectory of the listener's affective state from the current state to the target state”, ¶9, “identifying an affective trajectory from the current affective state to the target affective state”, ¶116, “current affective state 212 of the listener is plotted as a starting point 252 for the curve 250. The target affective state 214 is plotted as an endpoint 254 of the curve 250. One or more intermediate waypoints may be plotted along the curve 250, such as first waypoint 256 and second waypoint 258, indicating intermediate affective states on the affective trajectory 218. An initial portion 260 of the curve 250 is defined by the starting point 252 and first waypoint 256. A second subsequent portion 262 of the curve 250 is defined by the first waypoint 256 and the second waypoint 258. A third and final subsequent portion 264 of the curve 250 is defined by the second waypoint 258 and the endpoint 254. Machine learning techniques can also be implemented to learn the best trajectory for individuals using the system, making these trajectories dynamic based on previous success at achieving the user's target affective state”, ¶101, “two separate machine learning models to generate audio … One machine learning model is an affective inference model … other machine learning system is a reinforcement learning model with a deep learning neural network—also called a Deep Q Network (DQN)”) a plurality of actions (¶117, “audio segment identification process 222 is used to select or identify an audio segment … from a subset of the audio segments 220 stored in the audio library”, the plurality of actions are listening to any of the different possible songs that could be provided to the user to listen), and a first computer-implemented machine learning model, wherein:
the first computer-implemented machine learning model is configured to relate actions of the plurality of actions and changes in value in at least one of the first dimension and the second dimension of the multidimensional mental state model (Fig. 2A, ¶117, “audio segment identification process 222 is used to select or identify an audio segment that, when presented to the listener as an auditory stimulus, is likely to induce at least the initial portion 260 of the affective trajectory 218 in the affective state of the listener. The audio segment 230 is identified using a trained segment identification machine learning model, shown as DQN 120, that selects the audio segment 230 from a subset of the audio segments 220 stored in the audio library”);
the predicted path comprises one or more actions of the plurality of actions predicted by the first computer-implemented machine learning model to adjust the baseline mental state to the target mental state via the plurality of steps (¶8, “audio stream is generated by a machine learning model t… indicating the effectiveness of specific audio segments, or audio segments having specific features, in effecting the desired affective trajectory”, ¶9, “machine learning model to identify a first audio segment likely to induce in the listener a desired affective response corresponding to at least an initial portion of the affective trajectory”, ¶11, “machine learning model is used to identify a subsequent audio segment likely to induce in the listener a subsequent desired affective response corresponding to at least an initial portion of the updated affective trajectory when the subsequent audio segment is presented to the listener as an auditory stimulus. The audio stream is generated based at least in part on the first audio segment and the subsequent audio segment”);
each step of the plurality of steps describes a change to at least one of the first dimension and the second dimension of the multidimensional mental state model (Fig. 2A-2B, ¶11, “infer an inferred new affective state based on the current affective state and a set of audio feature values of the first audio segment. An updated affective trajectory from the inferred new affective state data to the target affective state is identified. The trained segment identification machine learning model is used to identify a subsequent audio segment “, ¶160, “updated state valence (the valence value of the inferred new affective state 226 or updated current affective state 212 prior to the current step), and updated state activation (the activation value of the inferred new affective state 226 or updated current affective state 212 prior to the current step)“, ¶117, “audio segment 230 is selected based on an assessment by the DQN 120 that the audio segment 230 is more likely than other audios segments in the subset of the audio segments 220 to induce at least the initial portion 260 of the affective trajectory 218 in the listener, i.e., that the audio segment 230, when played to the listener as an auditory stimulus, is likely to induce an affective state in the listener close to the state represented by the first waypoint 256 or one of the subsequent points 258, 254 on the affective trajectory”, ¶127);
the one or more actions are performable by the individual (¶117, user listen to the provided audio segment);
incrementally simulating the plurality of steps of the predicted path comprises:
simulating changes to the baseline mental state using the first computer implemented machine-learning model and the plurality of actions to identify a first step of the predicted path (Fig. 2A-2B, ¶11, “identify a first audio segment likely to induce in the listener a desired affective response corresponding to at least an initial portion of the affective trajectory in the listener when presented to the listener as an auditory stimulus”, ¶116, “starting point 252 for the curve 250 … first waypoint 256 … initial portion 260“), the first step comprising a first action of the first set of actions predicted to adjust the baseline mental state to a first preferred intermediate mental state (¶117, “…the audio segment 230, when played to the listener as an auditory stimulus, is likely to induce an affective state in the listener close to the state represented by the first waypoint 256 or one of the subsequent points 258, 254 on the affective trajectory”);
simulating changes to the first preferred intermediate mental state using the first computer-implemented machine-learning model and the plurality of actions to identify a second step of the predicted path (¶11, “ An updated affective trajectory from the inferred new affective state data to the target affective state is identified. The trained segment identification machine learning model is used to identify a subsequent audio segment“), the second step comprising a second action of the first set of actions predicted to adjust the first preferred intermediate mental state toward the target mental state (¶11, “trained segment identification machine learning model is used to identify a subsequent audio segment likely to induce in the listener a subsequent desired affective response corresponding to at least an initial portion of the updated affective trajectory …”, ¶116, “second subsequent portion 262 of the curve 250 is defined by the first waypoint 256 and the second waypoint 258. A third and final subsequent portion 264 of the curve 250 is defined by the second waypoint 258 and the endpoint 254”);
and outputting an indication of the one or more actions to the individual (¶117, providing audio segment to the user to listen).
Tao and D1 are analogous art to the claimed invention because they are from a similar field of endeavor of identifying and controlling emotional state. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Tao resulting in resolutions as disclosed by D1 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Tao as described above to provide and recommend music intended to induce specific changes in a listener's affective state (D1, ¶1).
With regard to Claim 2,
Tao-D1 disclose the method of claim 1, wherein:
the target mental state is identified based on a task performed by the individual (D1, ¶4, “Themes may also be organized around an activity that implies the targeting of certain affective states (calm music for cooking or meditating, aggressive music for working out, upbeat rhythmic music for dancing)”); and the video data depicts the individual performing the task (Tao, ¶17, “image feature extraction sub-step is configured to divide a video in the video file into several image frames, detect a location of face area from each of the image frames and extract a shape feature and an appearance feature based on the location of the face area, and calculate statistical information of shape features and appearance features of all image frames so as to obtain final image features”). The Examiner further notes that the [video data] is non-functional descriptive material and is not functionally involved in the steps recited. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See In re Gulack, 703 F.2d 1381, 218 USPQ 401, 403 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 3,
Tao-D1 disclose the method of claim 1, wherein predicting the baseline mental state comprises:
generating, by a second computer-implemented machine learning model, the first mental state value based on the first set of features; and generating, by a third computer-implemented machine learning model, the second mental state value based on the first set of features (D1, FIG16 1620-1622, ¶112, “Examples described herein will generally refer to a two-dimensional model of affect with valence and activation values. However, some embodiments may use other affect models, including models that use more or fewer than two dimensions to characterize affective states, models that use time-varying affective values to model affective states, and models that use a list of discrete affective states”, ¶147, “different machine learning models can be used in replacement of the neural networks shown in in FIGS. 4A-B and 5. The affective inference neural network 140 can be replaced by various forms of supervised and unsupervised machine learning systems while maintaining the same core inputs and outputs needed to infer the user's affective state based on a selection of music and/or audio. Similarly, the DQNs 120 can be replaced by various forms of supervised and unsupervised machine learning systems while maintaining the same core inputs and outputs”, ¶187).
The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 4,
Tao-D1 disclose the method of claim 3, wherein the first dimension describes an intensity of a first mental state and the second dimension describes a pleasantness of the first mental state (D1, ¶112, “circumplex model, where a given affective state is represented as a valence value (representing the degree of positive or negative emotion) and an arousal or activation value (representing the degree of emotional alertness or energy). In a two-dimensional valence-activation model of affect, for example, sadness might be represented by a negative valence and low activation, anger might be represented as negative valence and high activation, enthusiasm might be represented as positive valence and high activation, and relaxation might be represented as positive valence and low activation”).
The same motivation to combine for claim 1 equally applies for current claim
With regard to Claim 5,
Tao-D1 disclose the method of claim 3, wherein the first dimension describes an intensity of the first mental state, a pleasantness of the first mental state, an importance of information conveyed by the individual, a positivity of the conveyed information, or a subject of the conveyed information (D1, ¶112, “circumplex model, where a given affective state is represented as a valence value (representing the degree of positive or negative emotion) and an arousal or activation value (representing the degree of emotional alertness or energy). In a two-dimensional valence-activation model of affect, for example, sadness might be represented by a negative valence and low activation, anger might be represented as negative valence and high activation, enthusiasm might be represented as positive valence and high activation, and relaxation might be represented as positive valence and low activation”).
The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 6,
Tao-D1 disclose the method of claim 3, wherein the first dimension describes a first mental state, the second dimension describes a second mental state, and the first mental state and the second mental state are selected from a group consisting of tiredness, sleepiness, serenity, satisfaction, calmness, relaxation, contentment, distress, frustration, anger, annoyance, tension, fear, alarm, misery, sadness, depression, gloom, boredom, astonishment, amusement, excitement, happiness, delight, gladness, pleasure, thankfulness, gratitude, confusion, smugness, deliberation, anticipation, cheer, sympathy, trust, humor, envy, melancholy, hostility, resentment, revulsion, and ennui (D1, ¶112, “In a two-dimensional valence-activation model of affect, for example, sadness might be represented by a negative valence and low activation, anger might be represented as negative valence and high activation, enthusiasm might be represented as positive valence and high activation, and relaxation might be represented as positive valence and low activation. Examples described herein will generally refer to a two-dimensional model of affect with valence and activation values. However, some embodiments may use other affect models, including models that use more or fewer than two dimensions to characterize affective states”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 7,
Tao-D1 disclose the method of claim 1, wherein analyzing at least one of the image data, the audio data, and the semantic text data to identify the first set of features comprises: analyzing the image data to identify the first set of features; analyzing the audio data to identify a second set of features; and analyzing the semantic text data to identify a third set of features (TAO, ¶51, “feature extraction step 1 is configured to extract acoustic features, text features, and image features in a video file, and fuse the acoustic features, the text features and the image features in the video file to obtain multi-modal features”).
The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 8,
Tao-D1 disclose the method of claim 7, wherein predicting the baseline mental state comprises:
generating, by a second computer-implemented machine learning model, the first mental state value based on at least one of the first set of features, the second set of features, and the third set of features; and generating, by a third computer-implemented machine learning model, the second mental state value based on at least one of the first set of features, the second set of features, and the third set of features (TAO, ¶51, “feature extraction step 1 is configured to extract acoustic features, text features, and image features in a video file, and fuse the acoustic features, the text features and the image features in the video file to obtain multi-modal features”, D1, FIG16 1620-1622, ¶112, “Examples described herein will generally refer to a two-dimensional model of affect with valence and activation values. However, some embodiments may use other affect models, including models that use more or fewer than two dimensions to characterize affective states, models that use time-varying affective values to model affective states, and models that use a list of discrete affective states”, ¶147, “different machine learning models can be used in replacement of the neural networks shown in in FIGS. 4A-B and 5. The affective inference neural network 140 can be replaced by various forms of supervised and unsupervised machine learning systems while maintaining the same core inputs and outputs needed to infer the user's affective state based on a selection of music and/or audio. Similarly, the DQNs 120 can be replaced by various forms of supervised and unsupervised machine learning systems while maintaining the same core inputs and outputs”, ¶187).
The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 9,
Tao-D1 disclose the method of claim 7, wherein predicting the baseline mental state comprises: generating, by a second computer-implemented machine learning model, the first mental state value based on the first set of features and the second set of features; and generating, by a third computer-implemented machine learning model, the second mental state value based on the first set of features and the second set of features (TAO, ¶51, “feature extraction step 1 is configured to extract acoustic features, text features, and image features in a video file, and fuse the acoustic features, the text features and the image features in the video file to obtain multi-modal features”, D1, FIG16 1620-1622, ¶112, “Examples described herein will generally refer to a two-dimensional model of affect with valence and activation values. However, some embodiments may use other affect models, including models that use more or fewer than two dimensions to characterize affective states, models that use time-varying affective values to model affective states, and models that use a list of discrete affective states”, ¶147, “different machine learning models can be used in replacement of the neural networks shown in in FIGS. 4A-B and 5. The affective inference neural network 140 can be replaced by various forms of supervised and unsupervised machine learning systems while maintaining the same core inputs and outputs needed to infer the user's affective state based on a selection of music and/or audio. Similarly, the DQNs 120 can be replaced by various forms of supervised and unsupervised machine learning systems while maintaining the same core inputs and outputs”).
The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 10,
Tao-D1 disclose the method of claim 9, wherein:
the baseline mental state comprises a fifth mental state value corresponding to a third dimension of the multidimensional mental state model, the target mental state comprises a sixth mental state value corresponding to the third dimension of the multidimensional mental state model (D1, FIG16 1620-1622, ¶112, “Examples described herein will generally refer to a two-dimensional model of affect with valence and activation values. However, some embodiments may use other affect models, including models that use more or fewer than two dimensions to characterize affective states, models that use time-varying affective values to model affective states”, ¶176);
the first computer-implemented machine learning model is configured to relate actions of the plurality of actions and changes in value in at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model (D1, Fig. 2A, ¶117, “audio segment identification process 222 is used to select or identify an audio segment that, when presented to the listener as an auditory stimulus, is likely to induce at least the initial portion 260 of the affective trajectory 218 in the affective state of the listener. The audio segment 230 is identified using a trained segment identification machine learning model, shown as DQN 120, that selects the audio segment 230 from a subset of the audio segments 220 stored in the audio library”); and
the predicted path comprises one or more actions of the plurality of actions and corresponding changes to at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model (Fig. 2A, ¶117, “audio segment 230 is selected based on an assessment by the DQN 120 that the audio segment 230 is more likely than other audios segments in the subset of the audio segments 220 to induce at least the initial portion 260 of the affective trajectory 218 in the listener, i.e., that the audio segment 230, when played to the listener as an auditory stimulus, is likely to induce an affective state in the listener close to the state represented by the first waypoint 256 or one of the subsequent points 258, 254 on the affective trajectory”). The same motivation to combine for claim 1 equally applies for current claim
With regard to Claim 11,
Tao-D1 disclose the method of claim 10, wherein predicting the baseline mental state further comprises generating, by a fourth computer-implemented machine learning model, the fifth mental state value based on the third set of features (TAO, ¶51, “feature extraction step 1 is configured to extract acoustic features, text features, and image features in a video file, and fuse the acoustic features, the text features and the image features in the video file to obtain multi-modal features”, D1, FIG16 1620-1622, ¶112, “Examples described herein will generally refer to a two-dimensional model of affect with valence and activation values. However, some embodiments may use other affect models, including models that use more or fewer than two dimensions to characterize affective states, models that use time-varying affective values to model affective states, and models that use a list of discrete affective states”, ¶147, “different machine learning models can be used in replacement of the neural networks shown in in FIGS. 4A-B and 5. The affective inference neural network 140 can be replaced by various forms of supervised and unsupervised machine learning systems while maintaining the same core inputs and outputs needed to infer the user's affective state based on a selection of music and/or audio. Similarly, the DQNs 120 can be replaced by various forms of supervised and unsupervised machine learning systems while maintaining the same core inputs and outputs”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 14,
Tao-D1 disclose the method of claim 7, wherein analyzing the image data to identify the first set of features comprises analyzing the image data with a computer vision model (Tao, ¶55, “image feature extraction sub-step 13 is configured to divide the video into several image frames, detect a location of face area from each of the image frames and extract a shape feature and an appearance feature based on the location of the face area, and calculate statistical information of shape features and appearance features of all image frames so as to obtain final image features”, D1, ¶172). The same motivation to combine for claim 1 equally applies for current claim
With regard to Claim 16,
Tao-D1 disclose the method of claim 7, wherein analyzing the semantic text data to identify the third set of features comprises analyzing the semantic text data with a natural language understanding model (Tao, ¶54, “text feature extraction sub-step 12 is configured to convert each word in the video file to a corresponding word vector feature, and calculate statistical information of all word vector features so as to obtain sentence-level text features”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 19,
Tao-D1 disclose the method of claim 1, wherein outputting the indication of the one or more actions to the individual comprises:
cross-referencing, with a table of actions and instructions, the one or more actions to determine one or more instructions for performing the one or more actions (D1, ¶117, “audio segment identification process 222 is used to select or identify an audio segment … from a subset of the audio segments 220 stored in the audio library”, the audio library is a table that store set of audio segments, each represent an instructions for the action of listening to the song); and outputting the one or more instructions to the individual (D1, ¶117, “audio segment identification process 222 is used to select or identify an audio segment … from a subset of the audio segments 220 stored in the audio library”, providing the selected audio segment to the user where the audio segment is the instruction for performing an action of listening to the selected audio segment). The same motivation to combine for claim 1 equally applies for current claim
Regarding claim 20,
Claim 20 is similar in scope to claim 1; therefore it is rejected under similar rationale. Additionally, Tao-D1 disclose a processor; a user interface; and a memory encoded with instructions (Tao, ¶38, ¶¶78-79, D1, ¶¶108-109).
With regard to Claim 21,
Tao-D1 the method of claim 1, wherein:
the baseline mental state comprises a fifth mental state value (D1, ¶113, “ Additional biomarkers inferred from physiological data can also be used as inputs to the current state identification process, even beyond the two-dimensional valence and activation values, such as anxiety level, focus levels, agitation levels, etc.”);
the target mental state comprises a sixth mental state value (D1, ¶111, “… some embodiments may use other affect models, including models that use more or fewer than two dimensions to characterize affective states …”, ¶115, “A target state identification process 204 is used to identify the listener's target affective state 214”);
the fifth and sixth mental state values correspond to a third dimension of the multidimensional mental state model (D1, ¶111, “… some embodiments may use other affect models, including models that use more or fewer than two dimensions to characterize affective states …”, ¶113, “beyond the two-dimensional valence and activation values, such as anxiety level, focus levels, agitation levels, etc.”);
the first computer-implemented machine learning model is configured to relate actions of the plurality of actions and changes in value in at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model (D1, ¶117, “An audio segment identification process 222 is used to select or identify an audio segment that, when presented to the listener as an auditory stimulus, is likely to induce at least the initial portion 260 of the affective trajectory 218 in the affective state of the listener”, ¶11, “an affective inference process is used to infer an inferred new affective state based on the current affective state and a set of audio feature values of the first audio segment. An updated affective trajectory from the inferred new affective state data to the target affective state is identified”, ¶137); and
each step of the plurality of steps describes a change to at least one of the first dimension, the second dimension, and the third dimension of the multidimensional mental state model (D1, ¶11, “an affective inference process is used to infer an inferred new affective state based on the current affective state and a set of audio feature values of the first audio segment. An updated affective trajectory from the inferred new affective state data to the target affective state is identified”, ¶115, ¶136, “At step 308, a trained segment identification machine learning model (e.g. DQN 120) is used to identify a first audio segment (e.g. audios segment 230) likely to induce in the listener a desired affective response corresponding to at least an initial portion (e.g. initial portion 256) of the affective trajectory”, ¶137, “inferred new affective state data 226 is generated by the affect inference machine learning model 140 at step 310 and sent to the DQN 120 as a state data input”, ¶138, “If the method 300 determines at step 316 that the final step has not been reached, subsequent audio segments likely to induce subsequent portions of the affective trajectory are identified”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 22,
Tao-D1 the method of claim 21, wherein:
the baseline mental state comprises a seventh mental state value (D1, ¶113, “ Additional biomarkers inferred from physiological data can also be used as inputs to the current state identification process, even beyond the two-dimensional valence and activation values, such as anxiety level, focus levels, agitation levels, etc.”);
the target mental state comprises an eighth mental state value (D1, ¶111, “… some embodiments may use other affect models, including models that use more or fewer than two dimensions to characterize affective states …”, ¶115, “A target state identification process 204 is used to identify the listener's target affective state 214”);
the seventh and eighth mental state values correspond to a fourth dimension of the multidimensional mental state model (D1, ¶111, “… some embodiments may use other affect models, including models that use more or fewer than two dimensions to characterize affective states …”, ¶113, “beyond the two-dimensional valence and activation values, such as anxiety level, focus levels, agitation levels, etc.”);
the first computer-implemented machine learning model is configured to relate actions of the plurality of actions and changes in value in at least one of the first dimension, the second dimension, the third dimension, and the fourth dimension of the multidimensional mental state model (D1, ¶117, “An audio segment identification process 222 is used to select or identify an audio segment that, when presented to the listener as an auditory stimulus, is likely to induce at least the initial portion 260 of the affective trajectory 218 in the affective state of the listener”, ¶11, “an affective inference process is used to infer an inferred new affective state based on the current affective state and a set of audio feature values of the first audio segment. An updated affective trajectory from the inferred new affective state data to the target affective state is identified”, ¶137, “the affect inference process 224 uses the trained affect inference machine learning model 140 to predict how the audio segment selected by the deep learning neural network 120 at step 308 will affect the user. This inferred new affective state data 226 is generated by the affect inference machine learning model 140 at step 310 and sent to the DQN 120 as a state data input”); and
each step of the plurality of steps describes a change to at least one of the first dimension, the second dimension, the third dimension, and the fourth dimension of the multidimensional mental state model (D1, ¶11, “an affective inference process is used to infer an inferred new affective state based on the current affective state and a set of audio feature values of the first audio segment. An updated affective trajectory from the inferred new affective state data to the target affective state is identified”, ¶115, ¶136, “At step 308, a trained segment identification machine learning model (e.g. DQN 120) is used to identify a first audio segment (e.g. audios segment 230) likely to induce in the listener a desired affective response corresponding to at least an initial portion (e.g. initial portion 256) of the affective trajectory”, ¶137, “inferred new affective state data 226 is generated by the affect inference machine learning model 140 at step 310 and sent to the DQN 120 as a state data input”, ¶138, “If the method 300 determines at step 316 that the final step has not been reached, subsequent audio segments likely to induce subsequent portions of the affective trajectory are identified”). The same motivation to combine for claim 1 equally applies for current claim.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Tao et al. [US 2022/0270636 A1, hereinafter Tao] in view of Labbe [US 2023/0113072 A1, hereinafter D1] further in view of Chang et al. [US 2022/0028409 A1 hereinafter Chang].
With regard to Claim 15,
Tao-D1 teach the method of claim 7, wherein analyzing the audio data to identify the second set of features comprises: converting the audio data (Tao, ¶15, “acoustic feature extraction sub-step is configured to divide a voice in the video file into voice frames, extract an acoustic feature parameter of each voice frame, and calculate statistical information of acoustic feature parameters of all voice frames so as to obtain sentence-level acoustic features, wherein the acoustic feature parameters include at least one of prosodic feature, sound quality feature and spectral feature”, ¶53). The same motivation to combine for claim 1 equally applies for current claim.
Tao-D1 does not explicitly teach converting the audio data to a spectrogram; and analyzing the spectrogram with a fourth computer-implemented machine learning model.
Chang teach converting the audio data to a spectrogram; and analyzing the spectrogram with a fourth computer-implemented machine learning model (Claim 8, “audio unit detecting a sound around the infant to generate a plurality of audio samples; … a common model created from the audio samples; and an incremental model created from a plurality of infant crying features; and a processing unit connected to the audio unit and the memory and receiving the audio samples, wherein the processing unit converts the audio samples to generate a plurality of audio spectrograms, the processing unit extracts the audio spectrograms through the common model to generate a plurality of infant crying features, the processing unit trains the infant crying features through the incremental model to generate an identification result, and the processing unit judges whether the identification result is correct according to the real result”).
Tao-D1 and Chang are analogous art to the claimed invention because they are from a similar field of endeavor of sound identification. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Tao-D1 resulting in resolutions as disclosed by Chang with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Tao-D1 as described above to improve the application of machine learning to the system for sound identification, make the system avoid catastrophic forgetting effects and reduce the time for re-training and identification to train personalized models (Chang, ¶5).
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Tao et al. [US 2022/0270636 A1, hereinafter Tao] in view of Labbe [US 2023/0113072 A1, hereinafter D1] further in view of Komoto et al. [US 2012/0206597 A1 hereinafter Komoto].
With regard to Claim 17,
Tao-D1 teach the method of claim 1, wherein the simulating the predicted path comprises:
generating a first plurality of intermediate points based on the changes in value in at least one of the first dimension and the second dimension and the baseline mental state, wherein each intermediate point of the first plurality of intermediate points corresponds to an action of the one or more actions (D1, Fig. 2A, ¶116, “current affective state 212 of the listener is plotted as a starting point 252 for the curve 250. The target affective state 214 is plotted as an endpoint 254 of the curve 250. One or more intermediate waypoints may be plotted along the curve 250, such as first waypoint 256 and second waypoint 258, indicating intermediate affective states on the affective trajectory 218. An initial portion 260 of the curve 250 is defined by the starting point 252 and first waypoint 256. A second subsequent portion 262 of the curve 250 is defined by the first waypoint 256 and the second waypoint 258. A third and final subsequent portion 264 of the curve 250 is defined by the second waypoint 258 and the endpoint 254. Machine learning techniques can also be implemented to learn the best trajectory for individuals using the system, making these trajectories dynamic based on previous success at achieving the user's target affective state”, ¶117, “audio segment identification process 222 is used to select or identify an audio segment that, when presented to the listener as an auditory stimulus, is likely to induce at least the initial portion 260 of the affective trajectory 218 in the affective state of the listener);
determining a first preferred intermediate point of the first plurality of intermediate points, first plurality of intermediate points and the target mental state, and storing, as a first step of the predicted path, the change in value in at least one of the first dimension and the second dimension used to generate the first preferred intermediate point and the corresponding action of the one or more actions (D1, Fig. 2A, ¶116, “current affective state 212 of the listener is plotted as a starting point 252 for the curve 250. The target affective state 214 is plotted as an endpoint 254 of the curve 250. One or more intermediate waypoints may be plotted along the curve 250, such as first waypoint 256 and second waypoint 258, indicating intermediate affective states on the affective trajectory 218. An initial portion 260 of the curve 250 is defined by the starting point 252 and first waypoint 256. A second subsequent portion 262 of the curve 250 is defined by the first waypoint 256 and the second waypoint 258. A third and final subsequent portion 264 of the curve 250 is defined by the second waypoint 258 and the endpoint 254. Machine learning techniques can also be implemented to learn the best trajectory for individuals using the system, making these trajectories dynamic based on previous success at achieving the user's target affective state”, ¶117, “audio segment identification process 222 is used to select or identify an audio segment that, when presented to the listener as an auditory stimulus, is likely to induce at least the initial portion 260 of the affective trajectory 218 in the affective state of the listener”, as the system plot a curve including waypoints that represent the path between current and target affective states, and these trajectories dynamic based on previous success at achieving the user's target affective state. Then the system store a first step of the predicted path, the change in value used to generate the first preferred intermediate point and the corresponding action).
Tao-D1 does not explicitly teach measuring a first plurality of Euclidean distances between the first plurality of intermediate points and the target mental state; the first preferred intermediate point having a shortest Euclidean distance of the first plurality of Euclidean distances to the target mental state
Komoto teach generating a first plurality of intermediate points (Figs. 12-16, ¶132, “obtaining the geodesic distance by calculating a shortest path from the trajectory to another trajectory”,¶¶275-276, “As shown by trajectories "a" to "h" in FIG. 16A, even in the same object, the trajectory is different for each of the regions because of variations in posture”), measuring a first plurality of Euclidean distances between the first plurality of intermediate points and the target mental state (¶273, “Euclidean distance calculation unit 1501 and a clustering unit 1502 as shown in FIG. 15. The Euclidean distance calculation unit 1501 calculates a Euclidean distance between trajectories”, ¶¶275-276, “high similarity indicates that the Euclidean distance f (i, j) between the trajectory "i" and the trajectory "j" is short. Moreover, when the Euclidean distance f (i, j) is short, this can be understood that the trajectories "i" and "j" are located at a short distance from each other in a higher-dimensional space including the trajectories”);
determining a first preferred intermediate point of the first plurality of intermediate points, the first preferred intermediate point having a shortest Euclidean distance of the first plurality of Euclidean distances to the target mental state (¶¶275-276, “high similarity indicates that the Euclidean distance f (i, j) between the trajectory "i" and the trajectory "j" is short. Moreover, when the Euclidean distance f (i, j) is short, this can be understood that the trajectories "i" and "j" are located at a short distance from each other in a higher-dimensional space including the trajectories”, selection process is based on finding the point (trajectory) with smallest Euclidean distance in high dimension space).
Tao-D1 and Komoto are analogous art to the claimed invention because they are from a similar field of endeavor of identifying path between trajectories. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Tao-D1 resulting in resolutions as disclosed by Komoto with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Tao-D1 as described above to allow an accurate extraction of a moving object in a scene where, for example, a plurality of persons are walking, and there is a significant change in shape of such pedestrians due to changes in posture, size, etc. (Komoto, ¶9).
With regard to Claim 18,
Tao-D1-Komoto teach the method of claim 17, wherein simulating the predicted path further comprises:
generating a second plurality of intermediate points based on the changes in value in at least one of the first dimension and the second dimension and the first preferred intermediate point, wherein each intermediate point of the second plurality of intermediate points corresponds to an action of the one or more actions (D1, Fig. 2A, ¶116, “current affective state 212 of the listener is plotted as a starting point 252 for the curve 250. The target affective state 214 is plotted as an endpoint 254 of the curve 250. One or more intermediate waypoints may be plotted along the curve 250, such as first waypoint 256 and second waypoint 258, indicating intermediate affective states on the affective trajectory 218. An initial portion 260 of the curve 250 is defined by the starting point 252 and first waypoint 256. A second subsequent portion 262 of the curve 250 is defined by the first waypoint 256 and the second waypoint 258. A third and final subsequent portion 264 of the curve 250 is defined by the second waypoint 258 and the endpoint 254. Machine learning techniques can also be implemented to learn the best trajectory for individuals using the system, making these trajectories dynamic based on previous success at achieving the user's target affective state”, ¶117, “audio segment identification process 222 is used to select or identify an audio segment that, when presented to the listener as an auditory stimulus, is likely to induce at least the initial portion 260 of the affective trajectory 218 in the affective state of the listener”;
measuring a second plurality of Euclidean distances between the second plurality of intermediate points and the target mental state (Komoto, ¶273, “Euclidean distance calculation unit 1501 and a clustering unit 1502 as shown in FIG. 15. The Euclidean distance calculation unit 1501 calculates a Euclidean distance between trajectories”, ¶¶275-276, “high similarity indicates that the Euclidean distance f (i, j) between the trajectory "i" and the trajectory "j" is short. Moreover, when the Euclidean distance f (i, j) is short, this can be understood that the trajectories "i" and "j" are located at a short distance from each other in a higher-dimensional space including the trajectories”);
determining a second preferred intermediate point of the second plurality of intermediate points, the second preferred intermediate point having a shortest Euclidean distance of the second plurality of Euclidean distances to the target mental state ((D1, Fig. 2A, ¶116, “Machine learning techniques can also be implemented to learn the best trajectory for individuals using the system, making these trajectories dynamic based on previous success at achieving the user's target affective state”, Komoto, ¶273, “Euclidean distance calculation unit 1501 and a clustering unit 1502 as shown in FIG. 15. The Euclidean distance calculation unit 1501 calculates a Euclidean distance between trajectories”,¶¶275-276, “high similarity indicates that the Euclidean distance f (i, j) between the trajectory "i" and the trajectory "j" is short. Moreover, when the Euclidean distance f (i, j) is short, this can be understood that the trajectories "i" and "j" are located at a short distance from each other in a higher-dimensional space including the trajectories”, selection process is based on finding the point (trajectory) with smallest Euclidean distance in high dimension space); and
storing, as a second step of the predicted path, the change in value in at least one of the first dimension and the second dimension used to generate the second preferred intermediate point and the corresponding action of the one or more actions (D1, Fig. 2A, ¶116, “current affective state 212 of the listener is plotted as a starting point 252 for the curve 250. The target affective state 214 is plotted as an endpoint 254 of the curve 250. One or more intermediate waypoints may be plotted along the curve 250, such as first waypoint 256 and second waypoint 258, indicating intermediate affective states on the affective trajectory 218. An initial portion 260 of the curve 250 is defined by the starting point 252 and first waypoint 256. A second subsequent portion 262 of the curve 250 is defined by the first waypoint 256 and the second waypoint 258. A third and final subsequent portion 264 of the curve 250 is defined by the second waypoint 258 and the endpoint 254. Machine learning techniques can also be implemented to learn the best trajectory for individuals using the system, making these trajectories dynamic based on previous success at achieving the user's target affective state”, ¶117, “audio segment identification process 222 is used to select or identify an audio segment that, when presented to the listener as an auditory stimulus, is likely to induce at least the initial portion 260 of the affective trajectory 218 in the affective state of the listener”, as the system plot a curve including waypoints that represent the path between current and target affective states, and these trajectories dynamic based on previous success at achieving the user's target affective state. Then the system store a first step of the predicted path, the change in value used to generate the first preferred intermediate point and the corresponding action ). The same motivation to combine for claim 17 equally applies for current claim.
Response to Arguments
Applicant argue that Labbe fails to disclose incrementally identifying mental state changes based on preceding mental states. Instead, the method of Labbe provides a method in which waypoints along affective trajectory 218 are determined after affective trajectory 218 has been determined and are determined based on the already plotted affective trajectory. Labbe not only fails to disclose, teach, or suggest the steps of generating a predicted path recited by claim 1, but also teaches away from the method of amended claim 1.
Examiner respectfully disagrees,
First, the argument impose limitations that are not required by the claims. The claim expressly recite “Simulating” and “predicted path” to change mental state, which encompass model based generation of intermediate states. Thus the claim does not require determination based on actual observed mental state, nor does it exclude the use of a trajectory identified through a model. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., determination based on actual observed mental state, or exclude the use of a trajectory identified through a model) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Second, Labbe explicitly discloses that an “inferred new affective state” is generated based on the current affective state, and an “updated affective trajectory” is then identified from the that inferred state, followed by selection of a “subsequent audio segment” corresponding to the updated trajectory see at least ¶11, “… trained segment identification machine learning model is used to identify a subsequent audio segment likely to induce in the listener a subsequent desired affective response corresponding to at least an initial portion of the updated affective trajectory …”. Further Labbe explicitly states that the inferred affective state becomes the current affective state for the next audio segment prediction. Additionally, trajectories are described as dynamic based on previous success ¶116, “an affective trajectory process 216 identifies an affective trajectory 218 from the current affective state 212 to the target affective state 214 … making these trajectories dynamic based on previous success at achieving the user's target affective state”, maybe shaped by machine learning model based on affective feedback data collected over time ¶126, “machine learning model, for shaping the curve 250 to a user-dependent or user-independent shape based on affective feedback data collected over time” and may proceed through one or more intermediate affective states ¶127, “induce one or more intermediate affective states (e.g. waypoints 256, 258) along the affective trajectory 218 before inducing the final target affective state 214”. Accordingly, Labbe does not rely on a fixed already plotted trajectory, but Labbe performs iterative steps generation where each step is based on a preceding mental estimated step.
Third, the argument that Labbe teaches away from the method of amended claim 1 is not persuasive. The provided argument is conclusory and unsupported. The argument does not identify any disclosure in Labbe that teach away from the claimed method.
Applicant argue that Labbe fails to provide any details as to how affective trajectory 218 can be identified using a machine-learning-based technique.
Examiner respectfully disagrees, The claims does not require any specific implementation, and Labbe expressly disclose that trajectories maybe shaped and generated using machine learning techniques and affective feedback which meet the claim’s limitation. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., provide any details as to how affective trajectory 218 can be identified using a machine-learning-based technique) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant argue that the simulation steps cannot be practically performed in the human mind. Applicant notes that claim 1 does not merely require steps like "estimating mental state" and "predicting changes to mental state," but rather includes over two pages of limitations specifically and thoroughly detailing the manner in which mental state is calculated and actions for adjusting mental state are incrementally simulated.
Examiner respectfully disagrees, a human can mind can easily identify different mental states (e.g. happy-sad dimension, active-calm dimension), and can identify different action that could be taken to modify the value on one of these dimensions and can update the analysis to reach a specific state based on the previous action and its expected or noticed output. For example, user can see that person is so active and jumping with a group, mentally the user goal to calm this person could be by calculating different steps and actions that could be taken to calm the person as first isolating the person, then asking the person to sit down, and once the person is a little calm playing a calm music (simulated steps to reach a target mental state) which is mental process (Mental process, observation, evaluation and judgment). Examiner notes that there no similarity between example 38 and the proposed claims as example 38 did not recite Judicial exception but the current application does.
Applicant argue that the simulation steps cannot be practically performed in the human mind. As human mind cannot practically perform incremental simulations of changes to mental state through a mental state model having three or, especially, four dimensions.
Examiner respectfully disagrees, a human can mind can easily identify different mental states (e.g. happy-sad dimension, active-calm stressed-relaxed sleepy-alert dimension), and can identify different action that could be taken to modify the value on one of these dimensions and can update the analysis to reach a specific state based on the previous action and its expected or noticed output. For example, user can see that person is so active and jumping with a group, mentally the user goal to calm this person could be by calculating different steps and actions that could be taken to calm the person as first isolating the person, then asking the person to sit down, and once the person is a little calm playing a calm music (simulated steps to reach a target mental state) which is mental process (Mental process, observation, evaluation and judgment). Examiner notes that there no similarity between example 38 and the proposed claims as example 38 did not recite Judicial exception but the current application does.
Applicant argue that the ordered combination of all steps recited by the claim reflects the technical improvements discussed at, e.g., paragraphs [0013]-[0014] of the as-filed application. Therefore, claim 1, when evaluated as a whole, integrates any judicial exception recited by the limitations thereof into a practical application and satisfies Step 2A, Prong Two of the Subject Matter Eligibility Test similar to example 48.
Examiner respectfully disagrees, as disclosed in MPEP 2106 “Subject Matter Eligibility Test” Step 2B and as clarified in example 48 the evaluating is to the additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. The additional elements in the claims does not provide any limitations that could be considered significantly more than what is "well-understood, routine, or conventional". As the argued simulation steps are part of the abstract idea they cannot integrates any judicial exception recited by the limitations thereof into a practical application and will not satisfy Step 2B of the Subject Matter Eligibility Test. Therefore the arguments are not persuasive.
Claim 20 recites substantially parallel limitations to those of claim 1, therefore the same response to arguments provided for claim 1 apply to claim 20.
As to the remaining dependent claims, applicant argue that they are allowable due to their respective direct and indirect dependencies upon one of the aforementioned Independent claims. The examiner respectfully disagrees, Independent claims were not allowable as stated in the paragraph above in this “Response to Arguments” section in this office action.
Examiner respectfully withdraw 35 USC 112(b) rejection based on the presented claims amendments. However, new rejection under 35 USC 112(b) is provided based on the provided amendments.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
US Patent Application Publication No. 20230040657 filed by Zheng et al. that disclose the ability to convert audio data to spectrogram to be analyzed See at least Fig. 1, ¶6, “a method for instrument separating and reproducing for a mixture audio source, including converting the mixture audio source of selected music into a mixture audio source spectrogram, where the mixture audio source includes sound of at least one instrument; after that, putting the spectrogram into an instrument separation model to sequentially obtain an instrument feature mask of each of the at least one instrument from the mixture audio source, and obtaining an instrument spectrogram thereof based on the instrument feature mask of the each of the at least one instrument; then, determining an instrument audio source of the instrument based on the instrument spectrogram thereof”, ¶8, ¶19
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
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