Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 19 – 38 are pending and examined herein.
Claims 19, 26, 33 are rejected for double patenting.
Claims 36 – 38 are rejected under 35 U.S.C. 112(b).
Claims 19 – 38 are rejected under 35 U.S.C. 101.
Claims 19, 21, 26, 28, 33, 35 are rejected under 35 U.S.C. 102(a)(1).
Claims 20, 22 – 25, 27, 29 – 32, 34, 36 – 38 are rejected under 35 U.S.C. 103.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because
Reference character “103” has been used to designate both “Additional Local & Remote Circuitry” and “Neural Net” in Fig. 1
Reference character “301” has been used to designate both “Processing Circuitry” and “Selected Segment Topology” in Fig. 3
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
The abstract of the disclosure is objected to because the published abstract is approximately 226 words, which exceeds the 150 word limit. Also, the content of the abstract is repetitive. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The disclosure is objected to because of the following informalities.
Below are some examples for reference numbers from Fig. 1 – 4.
[0101] uses reference 133 to refer “episode pattern set”. Reference 335 is correct one
References 151, 253, 447 not described in specification
[0019] uses reference 187 to refer “cross segment” influence data while using 187 to refer “inter segment influence” in other paragraphs.
[0018] uses reference 103 to refer “additional local and remote circuitry” while using 103 to refer “neural network circuitry” in other paragraphs.
[0094] uses reference 235 to refer “the first segment random content pattern”.
[0111] uses reference 303 to refer both “memory circuits” and “processing circuitry”.
[0111] uses reference 343 to refer “the local and public data” while using 343 to refer “public and private data” in other paragraphs.
[0102] uses reference 301 to refer both “Processing Circuitry” and “Selected Segment Topology”
[0137] uses reference 407 to refer “processing and neural net circuitry”. Should use 401 instead.
[0127] uses reference 411 to refer “memory circuitry” while using 411 to refer “local support processing” in other paragraphs.
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 23, 30, 37 are objected to because of the following informalities: Claims 23, 30, 37 recite “one or more objective segment patterns, episodic random patterns and random content segment patterns”. If alternatives are intended, the phrase should apparently read “one or more of… “. Applicant should clarify whether one or more selected pattern types or all three pattern types are required. Appropriate correction is required.
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 19, 26, and 33 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 19 – 21 of copending Application No. 18/767,259 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because claim 19 of the co-pending application broadly recites first and second artificial intelligence based elements taking turns generating respective outputs within segments to accommodate internal segment or cross segment influence. This encompasses, or at least renders obvious, sequential generation in which a first output influences a subsequently generated output at the portion, sub-segment or segment level.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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 3638 are 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.
Claim 36 recites the limitation “the plurality of segment generation” in line 3. However, neither claim 33 nor claim 36 previously introduce “a plurality of segment generations”. Claim 33 recites a first segment topology, a second segment topology, a first generated segment output, and a second generated segment output. It is unclear whether “the plurality of segment generation” refers to the segment topologies, the operations that generate the segment outputs, or the generated segment outputs themselves. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the plurality of segment generation” is interpreted as referring to generation of the first and second generated segment outputs recited in claim 33.
Claims 37 – 38 are dependent on claim 36. They do not resolve the issue of indefiniteness and are rejected with the same rationale.
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 19 - 38 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP § 2109(III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject matter. The analysis of claims 19 – 38, in accordance with these steps, follows.
Step 1 Analysis:
Step 1 is to determine whether the claim is directed to a statutory category (process, machine, manufacture, or composition of matter.
Claims 19 – 38 are directed to an artificial intelligence infrastructure, meaning that it is directed to the statutory category of machine.
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
Regarding claim 19, the following claim elements are abstract ideas:
manage a plurality of segment generation; (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
managing a first of the plurality of segment generation with both a first portion generation … and a second portion generation… (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
wherein the first portion generation influences the second portion generation. (Considering information from an earlier portion when generating a subsequent portion is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
circuitry configured to …, and the circuitry … (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
based on a first artificial intelligence element, based on a second artificial intelligence element, (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 20, the rejection of claim 19 is incorporated herein. Further, claim 20 recites the following abstract idea:
operable to personalize operations across segments according to the artificial intelligence input influence data. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 20 further recites following additional elements
the artificial intelligence infrastructure comprises memory … and the circuitry is … (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
… operable to store artificial intelligence input influence data, (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 21, the rejection of claim 19 is incorporated herein. Further, claim 21 recites the following abstract idea:
… to influence the plurality of segment generation with user input data. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 21 further recites following additional elements
the artificial intelligence infrastructure comprises input and output circuitry operable … (This is mere data gathering and outputting, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 22, the rejection of claim 19 is incorporated herein. Further, claim 22 recites the following abstract idea:
… and the patterns are operable to influence the plurality of segment generation. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 22 further recites following additional elements.
the artificial intelligence infrastructure comprises memory operable to store patterns, (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 23, the rejection of claim 22 is incorporated herein. Further, claim 23 recites the additional elements:
the patterns comprise one or more objective segment patterns, episodic random patterns and random content segment patterns. (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 24, the rejection of claim 22 is incorporated herein. Further, claim 24 recites the following abstract idea:
the patterns provide influence data for elements within segments and sub-segments. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 24 does not recite additional elements
Regarding claim 25, the rejection of claim 24 is incorporated herein. Further, claim 25 recites the following additional elements:
the influence data comprise one or more of voice data, video data, text data, image data and audio data. (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 26, the following claim elements are abstract ideas:
manage a first sub-segment topology and a second sub-segment topology in delivering a generated segment of an overall segmented artificial intelligence based objective; (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
direct an initial sub-segment generated output based on the first sub-segment topology followed by a subsequent sub-segment generated output based on the second sub-segment topology, (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
the subsequent sub-segment generation being influenced by the initial sub-segment generated output. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
circuitry configured to …, and the circuitry being configured to… (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 33, the following claim elements are abstract ideas:
support a first segment topology and a second segment topology that correspondingly deliver a first generated segment output and a second generated segment output of an overall segmented artificial intelligence based objective; (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
direct the first generated segment output followed by the second generated segment output, (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
wherein the second generated segment output being influenced by the first generated segment output. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
circuitry configured to …, and the circuitry being configured to… (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Claims 27 - 32 recite substantially similar subject matter to claims 20 – 25 respectively and are rejected with the same rationale, mutatis mutandis.
Claims 34 - 38 recite substantially similar subject matter to claims 20 – 25 respectively and are rejected with the same rationale, mutatis mutandis.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 19, 21, 27, 28, 33, 35 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu et al. (U.S. Pub. 2023/0118966).
Regarding claim 19, Liu teaches
circuitry configured to manage a plurality of segment generation; ([0002] of Liu states “One aspect includes a computing system comprising a processor and memory. The processor can be configured to execute a program using portions of the memory to receive the user input, generate a story text based on the user input, generate a plurality of story images based on the story text, and output a story including the story text and a story video having content corresponding to the story text, wherein the story video includes the plurality of story images.” )
and the circuitry managing a first of the plurality of segment generation with both a first portion generation based on a first artificial intelligence element and a second portion generation based on a second artificial intelligence element, wherein the first portion generation influences the second portion generation. ([0012] of Liu states “In the depicted example, the story text generation module 116 includes a sequence-to-sequence transformer model 120 that can be used to generate a new sentence or phrase from a word, a phrase, a sentence, or multiple sentences such that the new sentence or phrase has contextual coherence with the user input from which it is generated.” [0013] of Liu states “The story video generation program 112 includes a story image generation module 122 that receives the story text 118. The story image generation module 122 uses the story text 118 to generate a plurality of story images 124. In some implementations, an image is generated for each sentence or phrase in the story text 118, where each image includes content related to its respective sentence. In other implementations, an image is generated for every two or more sentences and/or phrases. Various text-to-image generative models may be used to generate such images. For example, in the depicted example, the story video generation program 112 includes a generative diffusion model 126 for generating images using the story text 118. Diffusion models are a class of probabilistic generative models that can provide for processes that can generate new images within timeframes adequate for many different applications. Diffusion models typically involve two stages, a forward diffusion stage and a reverse denoising stage. In the forward diffusion process, input data is gradually altered and degraded over multiple iterations by adding noise at different scales. In the reverse denoising process, the model learns to reverse the diffusion noising process, iteratively refining an initial image, typically made of random noise, into a fine-grained colorful image.” The transformer constitutes the first artificial intelligence element or first sub-segment topology and generates story text as the first portion or initial sub-segment output. The diffusion model constitutes the second artificial intelligence element or second sub-segment topology and generates an image using the generated story text. Accordingly, the first generated output influences generation of the second generated output. )
Regarding claim 21, the rejection of claim 19 is incorporated herein. Furthermore, Liu teaches
the artificial intelligence infrastructure comprises input and output circuitry operable to influence the plurality of segment generation with user input data. ([0011] of Liu states “Upon execution by the processor 104, the instructions stored in the story video generation program 112 cause the processor 104 to initialize the video generation process, which includes receiving a user input 114. The user input 114 may be received via the I/O module 106. Generated story videos corresponding to user input 114 include videos having content related to the user input 114. The user input 114 may be provided in various formats. In some implementations, the user input 114 includes a selection based on presented options. In other implementations, the user input 114 includes provided text input. The text input can include one or more words, phrases, and/or sentences. Other forms of user input 114, such as audio input, image input, etc., can also be utilized. In some implementations, the user input 114 further includes information describing an artistic style in which the story video is to be generated. “ [0012] of Liu states “In some implementations, the predetermined number of iterations or length is provided by a user via the input/output (I/O) module 106 or any other suitable means. The sentence generation process can also be performed recursively, using the previous and newly generated sentences and/or phrases as input to the sequence-to-sequence transformer model 120. As can readily be appreciated, the user input 114 can initially include multiple sentences and/or phrases.” The user input influences the text generation topology because the story text generation module generates story text based on the user input. The user input also influences the image generation topology because the user input includes an artistic style descriptor and the story images are generated based on that descriptor. )
Claim 26 recites substantially similar subject matter as claim 19 respectively, and is rejected with the same rationale, mutatis mutandis.
Claims 28, 35 recite substantially similar subject matter as claim 21 respectively, and are rejected with the same rationale, mutatis mutandis.
Regarding claim 33, Liu teaches
circuitry configured to support a first segment topology and a second segment topology that correspondingly deliver a first generated segment output and a second generated segment output of an overall segmented artificial intelligence based objective; and the circuitry being configured to direct the first generated segment output followed by the second generated segment output, wherein the second generated segment output being influenced by the first generated segment output. ([0002] of Liu states “One aspect includes a computing system comprising a processor and memory. The processor can be configured to execute a program using portions of the memory to receive the user input, generate a story text based on the user input, generate a plurality of story images based on the story text, and output a story including the story text and a story video having content corresponding to the story text, wherein the story video includes the plurality of story images.” [0012] of Liu states “In the depicted example, the story text generation module 116 includes a sequence-to-sequence transformer model 120 that can be used to generate a new sentence or phrase from a word, a phrase, a sentence, or multiple sentences such that the new sentence or phrase has contextual coherence with the user input from which it is generated.” [0013] of Liu states “The story video generation program 112 includes a story image generation module 122 that receives the story text 118. The story image generation module 122 uses the story text 118 to generate a plurality of story images 124. In some implementations, an image is generated for each sentence or phrase in the story text 118, where each image includes content related to its respective sentence. In other implementations, an image is generated for every two or more sentences and/or phrases. Various text-to-image generative models may be used to generate such images. For example, in the depicted example, the story video generation program 112 includes a generative diffusion model 126 for generating images using the story text 118. Diffusion models are a class of probabilistic generative models that can provide for processes that can generate new images within timeframes adequate for many different applications. Diffusion models typically involve two stages, a forward diffusion stage and a reverse denoising stage. In the forward diffusion process, input data is gradually altered and degraded over multiple iterations by adding noise at different scales. In the reverse denoising process, the model learns to reverse the diffusion noising process, iteratively refining an initial image, typically made of random noise, into a fine-grained colorful image.” Liu teaches recursively generating a subsequent sentence using previously generated sentences as input. Liu further generates a corresponding image for each sentence. Therefore, an earlier sentence or image unit corresponds to the first generated segment output and a subsequently generated sentence or image unit corresponds to the second generated segment output. The earlier sentence influences generation of the subsequent sentence, which after the corresponding subsequent image is generated.)
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 20, 22, 24, 25, 27, 29, 31, 32, 34, 36, 38 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (U.S. Pub. 2023/0118966) in view of Knipp et al. (U.S. Pub. 20160225187).
Regarding Claim 20, the rejection of claim 19 is incorporated herein. Furthermore, Liu does not explicitly teaches
the artificial intelligence infrastructure comprises memory operable to store artificial intelligence input influence data, and the circuitry is operable to personalize operations across segments according to the artificial intelligence input influence data.
However, Knipp teaches that
the artificial intelligence infrastructure comprises memory operable to store artificial intelligence input influence data, and the circuitry is operable to personalize operations across segments according to the artificial intelligence input influence data. ([0050] of Knipp states “Some embodiments of storage 120 also stores user information 129, which may include, by way of example and not limitation, user preferences (e.g., favorite characters, themes, user(s)′ bedtime information), which may be used by storytelling engine 160 for determining story length and energy level; previous stories (including dynamic stories) presented to a user, which may be used for presenting a user's favorite story elements (e.g., characters, themes, etc.) more frequently or presenting new story elements (e.g., new plots, settings, characters, etc.);” [0098] of Knipp states “ Furthermore, in some embodiments, a child's (or listener's) favorite story elements (which may be determined based on user-provided information or how often those elements occur in stories) may be stored in user information 129. In these embodiments, story guide 150 might prompt the storyteller about whether to include those favorite story elements. Similarly, the story guide 150 may determine story elements that have not been used before or have not been used recently and provide those story elements to the storyteller (or directly to storytelling engine 160) in order to create a new story experience.” [0109] of Knipp states “Example storytelling engine 160 may operate with operating system 112 and knowledge representation component 115. Assembler 162 is generally responsible for assembling a story for presentation. Embodiments of assembler 162 can assemble a story based on user-provided information (which may be obtained via story guide 150), environmental and contextual information, available story resources and story logic (including logic associated with user information 129 such as favorite story elements, elements to avoid, frequency of story elements in previously presented stories, bedtime, or other user preferences, user settings, or user history information).” Knipp teaches storage containing user information, including user preferences and previously presented stories, and teaches using that information to select favorite story elements, introduce new story elements, and control story characteristics.)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings from Liu and Knipp. Liu teaches using AI models to generate story text and corresponding images, with the generated text influencing the image generation. Knipp teaches storing user preferences, prior story information, story logic, templates, and multimedia story elements and using that information to personalize and control story assembly. One with the ordinary skill in the art would have been motivated to incorporate the teachings of Knipp into Liu to personalize the generated content and control the structure and multimedia elements of successive segments. It would have been a predictable combination of known elements to produce personalized and structurally consistent AI generated story segments.
Regarding Claim 22, the rejection of claim 19 is incorporated herein. Furthermore, the combination of Liu and Knipp teaches
the artificial intelligence infrastructure comprises memory operable to store patterns, and the patterns are operable to influence the plurality of segment generation. ([0049] of Knipp states “Some embodiments of storage 120 also include story logic 127, which may include story guide content and information (e.g., instructions, conditions, guidelines, queries, choices, options, branches, etc.)… Story logic 127 may also include relationships, rules, parameters, story structure elements, paths, etc., which may be invoked by storytelling engine 160 for story creation, modification, and presentation.” [0053] of Knipp states “User interface component 185 generally facilitates creation of story content by a developer and may be embodied as a graphical user interface (GUI) and corresponding computer application.” [0054] of Knipp states “Libraries generator 184 generally facilitates creating story libraries, including story resources, such as videos, sounds, pictures, story lines, transitions, and other content, used for assembling or presenting stories and story logic (e.g., instructions, conditions, guidelines, relationships, parameters, story structure elements, paths, rules, etc., between one or more story resources or elements), which may be invoked for story creation and presentation.” Under BRI, Knipp’s story logic (relationships, rules, parameters, story structure elements, paths, etc.) are reusable patterns influencing how a story is assembled. Knipp teaches that story logic is stored in storage and invoked by the storytelling engine for story creation, modification, and presentation. )
Regarding Claim 24, the rejection of claim 22 is incorporated herein. Furthermore, the combination of Liu and Knipp teaches
the patterns provide influence data for elements within segments and sub-segments. ([0055] of Knipp states “Examples of story blocks or story threads are described in connection to FIGS. 6A-6C. At a high level, a story block includes a module of a story, such as a scene or scene-portion, with character(s), setting, sounds and images (which may be dependent on the character(s), rather than the particular story block), plot or character interactions including dialog, etc… A single story block may include information about character(s), setting(s), or other story elements, or may include templates or placeholders for such story elements.” [0060] of Knipp states “By way of example and not limitation, placeholders may be used for story elements including not only characters, settings, sound effects, visual images, animations, videos, etc., but also story logic; prompts or guidance provided by story guide 150; story blocks or threads; story element positions and/or motion paths (which may be predetermined from among a number of positions or motion paths and wherein the beginning or ending of the path is the placeholder to be determined from near storytelling time or from an environmental model); or any other aspect of storytelling content that may desired to be modified near storytelling time by storytelling engine 160.” [0109] of Knipp states “In some embodiments, assembler 162 identifies placeholders in a coded story or story blocks and populates the placeholders with appropriate story content, which may be determined, for example, based on metadata provided in the story blocks or instructions or tags in the coded story representation. In some embodiments, storytelling engine 160 calls story guide 150 to receive information from a user in order to determine content for filling a placeholder.” A story block representing a scene or portion of a scene with its template and placeholders, corresponds to a pattern associated with a segment or subsegment. The placeholders in Knipp specify the characters, settings, sounds, images, videos, or other elements to be included within the story block. In the proposed combination, the placeholder information and selected content would be supplied as input constraints to Liu’s AI modules, which influences the elements generated within Liu’s segments and sub-segments.)
Regarding Claim 25, the rejection of claim 24 is incorporated herein. Furthermore, the combination of Liu and Knipp teaches
the influence data comprise one or more of voice data, video data, text data, image data and audio data. ([0054] of Knipp states “Libraries generator 184 generally facilitates creating story libraries, including story resources, such as videos, sounds, pictures, story lines, transitions, and other content, used for assembling or presenting stories and story logic (e.g., instructions, conditions, guidelines, relationships, parameters, story structure elements, paths, rules, etc., between one or more story resources or elements), which may be invoked for story creation and presentation.” [0060] of Knipp states “By way of example and not limitation, placeholders may be used for story elements including not only characters, settings, sound effects, visual images, animations, videos, etc., but also story logic;”)
Claims 27, 29, 31, 32 recite substantially similar subject matter as claims 20, 22, 24, 25 respectively, and are rejected with the same rationale, mutatis mutandis.
Claims 34, 36, 38 recite substantially similar subject matter as claims 20, 22, 24 + 25 respectively, and are rejected with the same rationale, mutatis mutandis.
Claims 23, 30, 37 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (U.S. Pub. 2023/0118966) in view of Knipp et al. (U.S. Pub. 20160225187), further in view of Bringsjord et al. (U.S. Pub. 20080235576)
Regarding Claim 23, the rejection of claim 22 is incorporated herein. Furthermore, the combination of Liu and Knipp teaches
the patterns comprise one or more objective segment patterns ([0112] of Knipp states “Further, some embodiments of storytelling engine 160 may be configured to use story logic to generate story structures to follow or to use predetermined story structures in order to satisfy conditional requirements of story variables or parameters, including length, pace, emotional intensity (i.e., rising and falling action), how to use scenes, characters, themes, plots, etc.” Knipp teaches using prior story information to provide continuity and variation across successive stories. Knipp’s predetermined story structures correspond to objective segment patterns because they control the structure and characteristics of the generated story segments. )
However, the combination does not explicitly teach
episodic random patterns and random content segment patterns.
Bringsjord teaches that
episodic random patterns and random content segment patterns. ([0115] of Bringsjord states “A high-level story structure, represented in the inventive system as a story grammar, may be input or randomly selected.” [0435] of Bringsjord states “Thus, if a betrayal theme is selected, then the fact that betrayal was selected may in turn limit the system in terms of which specific characters (or plots, or settings, etc. having different characteristics) in, for example, a character database could be selected by a random generator to achieve variability and randomness.” Bringsjord randomly selects story branches, characters, plots, settings which corresponds to random content patterns used to populate individual generated story portions or segments. [0050] of Knipp states “Some embodiments of storage 120 also stores user information 129, which may include, by way of example and not limitation, user preferences (e.g., favorite characters, themes, user(s)′ bedtime information), which may be used by storytelling engine 160 for determining story length and energy level; previous stories (including dynamic stories) presented to a user, which may be used for presenting a user's favorite story elements (e.g., characters, themes, etc.) more frequently or presenting new story elements (e.g., new plots, settings, characters, etc.);” [0098] of Knipp states “Similarly, the story guide 150 may determine story elements that have not been used before or have not been used recently and provide those story elements to the storyteller (or directly to storytelling engine 160) in order to create a new story experience.” [0034] of Bringsjord states “With the unique and unobvious features of the present invention, story generation can take place automatically in which a plurality of artifacts are generated in a specified language which humans are likely to find interesting. Further, the story generation is provided by a creative agent which begins with a seed of interestingness and maintains that theme (e.g., anchors the theme) in the generation of skillful variations that are sufficiently distinct from the input.” [0109] of Bringsjord states “While large and varied knowledge sources will lead to varied, well-structured artifacts, the theme anchors the creative agent to producing “interesting” artifacts where interesting is ultimately defined subjectively by the human composing the theme. The theme performs such anchoring by constraining subsequent choices made by the controller 100C/random generator in the plot, characters, dialogue, setting, literary structure, etc.” Knipp teaches using prior story information to retain recurring elements or introduce new elements in successive stories, while Bringsjord maintains an anchored theme that constrains randomly selected story content. In combination, providing recurring features across successive stories together with randomly selected new content corresponds to episodic random pattern.)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings from Liu, Knipp, Bringsjord. Liu teaches using AI models to generate story text and corresponding images, with the generated text influencing the image generation. Knipp teaches storing user preferences, prior story information, story logic, templates, and multimedia story elements and using that information to personalize and control story assembly. Bringsjord teaches randomly selecting story elements from stored databases while using an anchored theme to constrain the random selections. One with the ordinary skill in the art would have been motivated to incorporate the teachings of Bringsjord into the combination of Liu and Knipp to increase variation in the selected story content while maintaining continuity and coherence across successive stories. It would have been a predictable combination to produce structured AI generated story segments having recurring features and randomly varied content.
Claims 30, 37 recite substantially similar subject matter as claim 23 respectively, and are rejected with the same rationale, mutatis mutandis.
Conclusion
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/BYUNGKWON HAN/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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