DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
Claims 1, 21 and 22 recites the phrase “so that the metadata represents a degree of preference…” The term is not reasonably clear to a person of ordinary skill in the in art and lacks any objective boundary or defined measurement. The claim uses the phrase “degree of preference” without a defined scale or metric. Examiner is unclear as to whether the degree is numerical, categorical, probabilistic, heuristic etc. Therefore, the Examiner cannot determine the basis for the “degree of preference” or how to measure it.
Since claims 2-20 are directly dependent or indirectly dependent on claim 1, they inherit the same problem.
Claim Rejections - 35 USC § 101
The claim amendment to independent claims 1, 21 and 22 does not overcome a 35 U.S.C. 101 rejection. Adding a “machine learning or other generative AI model server” does not, by itself, supply a technological improvement or an inventive concept. The claim still reads as collecting data, analyzing data, generating text, which is an abstract idea implemented on generic computer components.
The claim remains ineligible because: it is still directed to data collection, data analysis, and content generation which are all abstract ideas. The “machine learning or generative AI model” is used as a black box without any improvement to the model, training, architecture, or computer functionality. The network transmission step is generic and does not integrate the abstract idea into a practical application. The claim lacks any inventive concept beyond routine ML inference.
Step 2A, Prong One Is still directed to an Abstract Idea
The core operations are obtaining user data, weighting content attributes, forming a request, sending the request to a model server and generating persona text using ML. These fall into recognized abstract idea categories of mental processes (analyzing preferences and generating descriptive text), Mathematical concepts (weighed metadata, preference scoring), Certain methods of organizing human activity (personalization and recommendation) and Generic computer implementation (sending data to a server and receiving generated text). The presence of a machine learning model does not change the nature of the idea. Courts and the USPTO repeatedly hold that invoking ML/AI does not avoid abstraction unless the claim improves the model or computer itself.
Step 2A, Prong Two Still has No Practical Application
The amended claims do not recite an improvement to machine learning architecture, an improvement to networking, an improvement to data structures, an improvement to content recommendation systems. Instead, the machine learning model is used in a purely functional way i.e., “generating a request for generating the descriptive persona text based on the user data”.
Step 2B Still has No Inventive Concept
Courts have held that using ML to classify or generate text is not an inventive concept. Sending data to a remote server is not an inventive concept. Weighting attributes and generating descriptive text is not an inventive concept.
The amendment adds location of processing (server) and the type of processing (ML/AI), but not how the technology is improved. To overcome the 101 rejection, the Applicant needs to have support in the specification that does something like “a specific improvement to model training or inference”, “a novel data structure enabling more efficient persona generation”, “a technical improvement to latency, memory usage, or accuracy”, “a hardware-accelerated or sensor-integrated pipeline” etc..
Regarding claims 2-20, the dependent claims do not amount to significantly more than the abstract idea because the merely provide additional insignificant extrasolution activities/steps of displaying generated descriptive persona text and are essential part of the abstract idea.
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 1-22 are rejected under 35 U.S.C. 101 because the claims are not directed to patent eligible subject matter. Regarding claims 1, 21 and 22:
Step 1: Statutory Category – The claim recites “a computer-implemented method”, which is a statutory category under 35 USC § 101.
Step 2A, Prong One: Judicial Exception
The claim recites a method for generating descriptive persona text by obtaining user data, analyzing content metadata and associated weightings and generating a persona title and description based on such data. The limitations, when viewed individually and in combination, describe collecting information, analyzing information, and generating textual output, which are mental processes and certain methods of organizing human activity (e.g., marketing, user profiling, and personalization) and mathematical concepts (the “weightings” representing user preference are mathematical relationships used to generate a textual output). As such, the claim is directed to an abstract idea under the 2019 Revised Patent Subject Matter Eligibility Guidance.
Step 2A, Prong Two: Integration into a Practical Application
The claim does not integrate the abstract idea into a practical application because the recited computer components (e.g., “computer-implemented method,” “content recommendation system”) are used merely as generic tools to perform the abstract idea, and the claim does not improve the functioning of a computer or any other technology. The claim does not add new data structure, no new algorithm technique, no specific technical architecture, no unconventional processing, no transformation of data into a technical result and no improvement to recommendation system mechanics. See MPEP 2106.04(d), 2106.05(f). The computer is merely used as a generic tool to gather data, apply weightings and generate text. This is exactly the type of “apply it on a computer” implementation that courts routinely reject (e.g., Electric Power Group, Affinity Labs, Customedia, Intellectual Ventures).
Therefore, the claim does not integrate the abstract idea into a practical application.
Step 2B: Inventive Concept
The claim does not recite additional elements that amount to significantly more than the abstract idea itself. The recited steps of obtaining data, applying weightings, and generating descriptive text are well-understood, routine, and conventional activities previously known in the field, and the claim does not include any unconventional technical feature or inventive concept that would transform the abstract idea into patent-eligible subject matter.
Accordingly, claims 1, 21 and 22 are directed to non-statutory subject matter.
Regarding claims 2-20, the dependent claims do not amount to significantly more than the abstract idea because the merely provide additional insignificant extrasolution activities/steps of displaying generated descriptive persona text and are essential part of the abstract idea.
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 1-5, 7-13 and 18-22 are rejected under 35 U.S.C. 103 as being unpatentable over Kasmi et al. (U.S. Pub. No. 2025/0209484) in view of Kong et al. (U.S. Pub. No. 2025/0209544).
Regarding claim 1, Kasmi et al. discloses a computer-implemented method for generating a descriptive persona text for one or more users of a content recommendation system comprising:
obtaining user data and/or associated content metadata for the one or more users, wherein the user data comprises content metadata associated with a plurality of content items available for through the content recommendation system (see paragraphs 0033-0035; user data includes behavioral interactions with items. Item listings include metadata such as price, reviews, images, rating, size color),
wherein the user data and/or associated content metadata is based on user activity engagement with the plurality of content items for of the one or more users (see paragraphs 0034-0035; user interactions with items: viewing, bidding, watching, adding to cart, purchasing).
However, Kasmi et al. is silent as to wherein the content metadata comprises a plurality of content attributes associated with the plurality of content items and associated weightings so that the metadata represents a degree of preference of the one or more users to the content attributes and generating the descriptive persona text for the one or more users based on the user data and/or associated content metadata, wherein the descriptive persona text is generated based on at least the weighting of the content attributes of the metadata for the one or more users, wherein the generated descriptive persona text comprises a persona title and a persona description.
Kong et al. discloses wherein the content metadata comprises a plurality of content attributes associated with the plurality of content items and associated weightings so that the metadata represents a degree of preference of the one or more users to the content attributes (see paragraphs 0003-0004, 0021, 0025; determining relevance scores using user engagement and attributes associated with content events. These relevance scores inherently weight attributes to represent likelihood of user engagement) and
generating the descriptive persona text for the one or more users based on the user data and/or associated content metadata (see fig. 11A-11B, paragraphs 0015-0016, 0111-0113; using a generative model to produce text outputs. Although Kong generates text for content events, not personas, it teaches the use of a generative model conditioned on user engagement and content attributes, which would motive generating descriptive text summarizing user preferences), wherein the descriptive persona text is generated based on at least the weighting of the content attributes of the metadata for the one or more users (see paragraphs 0003-0004, 0021, 0025; these relevance scores function as weightings of content attributes),
wherein the generated descriptive persona text comprises a persona title and a persona description (see fig. 11A-11B; generative text output via a generative model (figs. 11a-11b), which would reasonably be used to generate structured text such as a title and description).
It would have been obvious to a skilled artisan before the effective filing date of the claimed invention to modify the system of Kasmi et al. with the teachings of Kong et al., to generate descriptive persona text based on weighted content attributes to improve personalization.
Regarding claim 21, claim 21 is rejected for the same reason set forth in the rejection of claim 1.
Regarding claim 22, claim 22 is rejected for the same reason set forth in the rejection of claim 1.
Regarding claim 2, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). Kong et al. discloses wherein the method further comprise displaying and/or storing the generated descriptive persona text (see paragraph 0004, fig. 3b (step 316), fig. 11a (step 1106)).
Regarding claim 3, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). Kasmi et al. discloses obtaining user data for a plurality of users (see paragraphs 0022, 0034-0036), performing a clustering and/or grouping process on said user data to identify one or more clusters and/or groups of users (see paragraph 0024) and generating the descriptive persona text for each identified group based on the user data for the identified cluster and/or group (see paragraphs 0004, 0024, 0060-0062).
Regarding claim 4, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). Kasmi et al. et al. discloses identifying one or more groups of users and aggregating user data for users of said one or more groups and wherein the generation of the descriptive persona text is based on said aggregated user data (see paragraphs 0024, 0060-0062 and fig. 3).
Regarding claim 5, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). Kasmi et al. discloses wherein generating the persona text for one or more users comprises identifying a persona category from a set of pre-determined persona categories based on the user data for the one or more users (see paragraphs 0021, 0024, 0060-0062). Kong et al. discloses generating the descriptive persona text based on at least the identified persona (see paragraphs 0003-0004, 0021, 0025, figs. 11a-11b).
Regarding claim 7, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). Kasmi et al. discloses wherein the descriptive persona text comprises one or more references to a content item and/or characteristics of a content item that the user has previously engaged with (see paragraph 0022, 0024, 0034 and fig. 6).
Regarding claim 8, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). Kasmi et al. discloses wherein the descriptive persona text comprises references to content and/or characteristics of content that the user is likely to be interested in (see paragraphs 0002, 0024 and fig. 6).
Regarding claim 9, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). Kasmi et al. discloses wherein generating the descriptive persona text comprises applying a model, for example, a machine learning or other generative artificial intelligence model to at least part of the user data, optionally to an identified persona category, wherein the machine learning model is configured to output the descriptive persona text (see paragraphs 0002-0004, 0023-0024 and fig. 6).
Regarding claim 10, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). Kasmi et al. discloses wherein the model comprises a large language model and/or a natural language processing model and/or a machine learning and/or artificial intelligence model (see paragraph 0023-0024, 0061).
Regarding claim 11, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). Kasmi et al. discloses wherein the user data and/or associated metadata is represented as a feature vector or other data structure and wherein the method comprises generating a prompt or other input for a model based on said feature vector or other data structure (see paragraphs 0021-0023, 0034, 0066-0068).
Regarding claim 12, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). Kasmi et al. discloses wherein generating the descriptive text may comprise packaging at least part of said user data and/or associated content metadata and one or more selected parameters into one or more requests and optionally transmitting said one or more requests to a further computing resource (see paragraphs 0024, 0029, 0036, 0060, 0066-0068).
Regarding claim 13, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). Kasmi et al. discloses wherein the method comprises performing a filtering and/or selection process on the user data and/or associated metadata and wherein the generating of the descriptive persona text is based on the filtered and/or selected user data and/or associated metadata (see paragraphs 0022, 0034, 0060-0068).
Regarding claim 18, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). Kasmi et al. discloses wherein the method further comprises using at least part of the generated persona text to obtain one or more content item recommendation (see paragraphs 0014, 0024, 0026).
Regarding claim 19, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). Kasmi et al. discloses wherein the method further comprises displaying the one or more content item recommendations together with at least part of the generated persona text (see paragraphs 0024, 0026, 0033).
Regarding claim 20, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). Kasmi et al. discloses wherein the descriptive persona text comprises a non-attributable description of a user based on their user activity (see paragraph 0024).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Kasmi et al. and Kong et al. as applied to claim 1 above, and further in view of Nagaraja et al. (U.S. Pub. No. 2020/0042646).
Regarding claim 6, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). However, Kasmi et al. and Kong et al. are silent as to wherein generating the descriptive persona comprises obtaining a default persona text corresponding to a persona category and performing a modification of the default persona text based on the user data and/or associated content metadata.
Nagaraja et al. discloses wherein generating the descriptive persona comprises obtaining a default persona text corresponding to a persona category and performing a modification of the default persona text based on the user data and/or associated content metadata (see paragraph 0035).
It would have been obvious to a skilled artisan before the effective filing date of the claimed invention to modify the system of Kasmi et al. and Kong et al. with the teachings of Nagaraja et al., the motivation being to customize a template.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Kasmi et al. and Kong et al. as applied to claim 1 above, and further in view of Wu et al. (U.S. Pub. No. 2015/0161086).
Regarding claim 14, Kasmi et al. and Kong et al. discloses everything claimed as applied above (see claim 1). However, Kasmi et al. and Kong et al. are silent as to performing a validation process on the generated text and discarding and/or modifying and/or regenerating the descriptive text based on the outcome of the validation process.
Wu et al. discloses performing a validation process on the generated text and discarding and/or modifying and/or regenerating the descriptive text based on the outcome of the validation process (see paragraph 0073).
It would have been obvious to a skilled artisan before the effective filing date of the claimed invention to modify the system of Kasmi et al. and Kong et al. with the teachings of Wu et al., the motivation being to provide accuracy of data.
Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kasmi et al., Kong et al. and Wu et al. as applied to claim 14 above, and further in view of Lee et al. (U.S. Pub. No. 2012/0066234).
Regarding claim 15, Kasmi et al., Kong et al. and Wu et al. discloses everything claimed as applied above (see claim 14). However, Kasmi et al., Kong et al. and Wu et al. are silent as to wherein the validation process comprises evaluating a semantic similarity between the user data and the generated text.
Lee et al. discloses wherein the validation process comprises evaluating a semantic similarity between the user data and the generated text (see paragraph 0056).
It would have been obvious to a skilled artisan before the effective filing date of the claimed invention to modify the system of Kasmi et al., Kong et al. and Wu et al. with the teachings of Lee et al., the motivation being to provide a match between vectors.
Regarding claim 16, Kasmi et al., Kong et al. and Wu et al. discloses everything claimed as applied above (see claim 14). However, Kasmi et al., Kong et al. and Wu et al. are silent as to wherein the validation process comprises constructing a vector or other representation of the generated descriptive text and comparing said representation to a corresponding representation of the user data.
Lee et al. discloses wherein the validation process comprises constructing a vector or other representation of the generated descriptive text and comparing said representation to a corresponding representation of the user data (see paragraph 0055-0056).
It would have been obvious to a skilled artisan before the effective filing date of the claimed invention to modify the system of Kasmi et al., Kong et al. and Wu et al. with the teachings of Lee et al., the motivation being to provide a match between vectors.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Kasmi et al., Kong et al. and Wu et al. as applied to claim 14 above, and further in view of Root et al. (U.S. Pub. No. 2015/0372963).
Regarding claim 17, Kasmi et al., Kong et al. and Wu et al. discloses everything claimed as applied above (see claim 14). However, Kasmi et al., Kong et al. and Wu et al. are silent as to wherein the validation process comprises identifying pre-determined stop words in the generated text and discarding and/or modifying the generated text in response to identifying a stop word.
Root et al. discloses wherein the validation process comprises identifying pre-determined stop words in the generated text and discarding and/or modifying the generated text in response to identifying a stop word (see paragraph 0077).
It would have been obvious to a skilled artisan before the effective filing date of the claimed invention to modify the system of Kasmi et al., Kong et al. and Wu et al. with the teachings of Root et al., the motivation being to replace misspelt words.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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NNENNA EKPO
Primary Examiner
Art Unit 2425
/NNENNA N EKPO/Primary Examiner, Art Unit 2425 April 17, 2026.
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