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 .
The instant application having Application No. 19/290,961 filed on 8/5/2025 is presented for examination by the Examiner. Claims 1-20 are currently pending in the present application.
Priority
As required by M.P.E.P. 201.14(c), acknowledgement is made of Applicant's claim for priorities based on a PRO of 63/736,913 filed on 12/20/2024, and a PRO of 63/679,461 filed on 8/4/2024.
Drawings Objection
A descriptive textual label for each numbered element in these figures would be needed to fully and better understand these figures without substantial analysis of the detailed specification. Any structural detail that is of sufficient importance to be described should be shown in the drawing. Optionally, Applicant may wish to include a table next to the present figure to fulfill this requirement. See 37 CFR 1.83. 37 CFR 1.84(n)(o) is recited below:
(n) Symbols. Graphical drawing symbols may be used for conventional elements when appropriate. The elements for which such symbols and labeled representations are used must be adequately identified in the specification. Known devices should be illustrated by symbols which have a universally recognized conventional meaning and are generally accepted in the art. Other symbols which are not universally recognized may be used, subject to approval by the Office, if they are not likely to be confused with existing conventional symbols, and if they are readily identifiable.
(o) Legends. Suitable descriptive legends may be used, or may be required by the Examiner, where necessary for understanding of the drawing, subject to approval by the Office. They should contain as few words as possible."
The drawings are objected to because some elements or boxes (i.e., items in Figures 5B-5D and 9) have no suitable descriptive legends, which should contain as few words as possible, where necessary for understanding of the drawing.
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 1-20 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.
As per claims 1, 12 and 20; the claims recite the term/phrase “LLM” which renders the claims indefinite for failing to particularly point out and distinctly claim the invention. Clarification or correction is respectfully required.
Note, the dependent claims are also rejected because they depend on and/or do not remedy the deficiencies inherited by their parent claims.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per claim 1, the claim recites “A computer-implemented method for operating a persona-driven intelligence platform comprising:
responsive to receiving one or more inputs and/or instructions corresponding to a digital task, retrieving, from a database in memory of a computing system, a plurality of data records, each data record including a first set of attributes;
clustering the plurality of data records to generate a plurality of clusters of data records, based on the first set of attributes;
obtaining, for each of the plurality of clusters, a corresponding second set of attributes;
generating, for each of the plurality of clusters, a corresponding personality score vector, based on the second set of attributes;
generating, by one or more LLM agents, a dynamic persona corresponding to at least one of the plurality of clusters, the dynamic persona including a third set of attributes; and
storing the dynamic persona in a persona library”.
Step 1: Statutory Category
Claim 1 discloses a method which is a process within the meaning of the section.
Step 2A – Prong One: Judicial Exception Recited
The claim recites the limitations “clustering the plurality of data records to generate a plurality of clusters of data records, based on the first set of attributes”, “obtaining, for each of the plurality of clusters, a corresponding second set of attributes”, and “generating, for each of the plurality of clusters, a corresponding personality score vector, based on the second set of attributes”. The limitations are processes that, under its broadest reasonable interpretation, cover performance of the limitations in the mind, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in a human mind or with the aid of pen or paper. For example, “clustering…” and “obtaining…” in context of this claim encompass a user mentally, and with the aid of pen and paper looking at attributes of data records to evaluate and generate clusters of the data records, and selecting the desired or relevant attributes for each of the clusters of the data records; and the “generating… a corresponding personality score vector…” step is directed a mathematical concept which is an abstract idea.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion). Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: Integrated into a Practical Application
The claim recites additional elements: “responsive to receiving one or more inputs and/or instructions corresponding to a digital task, retrieving, from a database in memory of a computing system, a plurality of data records, each data record including a first set of attributes”, “generating, by one or more LLM agents, a dynamic persona corresponding to at least one of the plurality of clusters…” and “storing the dynamic persona in a persona library”. The judicial exception is not integrated into a practical application. In particular, the “receiving… retrieving…” and “generating, by one or more LLM agents, a dynamic persona…” steps mount to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and the “storing…” step is considered a mere instruction to apply an exception on a generic computer (see MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g).
Step 2B: Claim provides an Inventive Concept
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activities identified above, which include the data-gathering and the “generating, by one or more LLM agents, a dynamic persona corresponding to at least one of the plurality of clusters…” and “storing the dynamic persona in a persona library” steps are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)(II), and MPEP 2106.05(d)(II)(iv). For these reasons, there is no inventive concept in the claim, and thus it is ineligible.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of performing the “generating, by one or more LLM agents, a dynamic persona corresponding to at least one of the plurality of clusters…” and “storing the dynamic persona in a persona library” steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim as a whole, does not amount to significantly more than the abstract idea itself. This is because the claim does not affect an improvement to the functioning of a computer itself; and the claim does not move beyond a general link of the use of an abstract idea to a particular technological environment.
Accordingly, claim 1 is directed to an abstract idea.
As per claim 2, the claim recites “The method of claim 1, further comprising:
responsive to receiving the one or more inputs, generating a targeted digital communication corresponding to the digital task, wherein generating the targeted digital communication comprises:
generating, by the one or more LLM agents a personalized content for the targeted digital communication, based on the third set of attributes; and
delivering the targeted digital communication including the personalized content to a group of users associated with the at least one of the plurality of clusters via an electronic distribution channel”. The judicial exception is not integrated into a practical application. In particular, the additional limitations of “receiving” and “generating” mount to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)) and does not amount to significantly more than the above-identified judicial exception, and the additional limitation of “delivering” amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 3, the claim recites “The method of claim 1, wherein the plurality of data records is retrieved from the database based on a persona relevance score (PRS), the PRS corresponding to a likelihood of a positive user engagement with respect to the digital task. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 4, the claim recites “The method of claim 3, wherein the PRS is calculated based on engagement metrics corresponding to historical user interactions captured in the plurality of data records, the engagement metrics including at least: a place order rate; a total number of recipients; an open rate; a click rate; and a non-engagement data”. The judicial exception is not integrated into a practical application. In particular, this additional limitation covers mathematical concepts. Accordingly, the claim recites an abstract idea.
As per claim 5, the claim recites “The method of claim 1, wherein the plurality of data records that are retrieved from the database correspond to one or more core personas stored in the persona library”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 6, the claim recites “The method of claim 1, wherein obtaining the second set of attributes comprises: for each of the plurality of clusters: aggregating the corresponding data records to generate the second set of attributes”. The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)) and does not amount to significantly more than the above-identified judicial exception.
As per claim 7, the claim recites “The method of claim 1, wherein the personality score vector for each of the plurality of clusters is generated by a fine-tuned LLM”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 8, the claim recites “The method of claim 1, wherein the personality score vector for each of the plurality of clusters is generated by a trained classification model”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 9, the claim recites “The method of claim 1, wherein the personality score vector is a numerical representation of one or more personality traits corresponding to an OCEAN personality model or a 16 personalities model”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 10, the claim recites “The method of claim 1, wherein the third set of attributes includes at least one of: a brand story; demographics; psychographics; engagement guidelines; behavior guidelines; tone and style of textual content; communication recommendations; a persona name; a persona image; a persona video; persona features; a persona audio; or a persona storyline”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 11, the claim recites “The method of claim 1, wherein the one or more LLM agents are multi-agents”. The judicial exception is not integrated into a practical application. In particular, this additional limitation amounts to no more than mere instructions to apply an exception to perform an existing process on a generic computer (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer do not amount to significantly more.
As per claim 12, the claim recites “A system comprising: one or more processor devices; and one or more memories storing machine-executable instructions, which when executed by the one or more processor devices, cause the system to:” perform the similar limitations in the claim 1.
Step 1: Statutory Category
Claim 12 discloses a system which is a machine within the meaning of the section.
Step 2A – Prong One: Judicial Exception Recited
The claim recites the limitations as same as the claim 1, and therefore are interpreted as an abstract idea under the same premise as the claim 1.
Step 2A – Prong Two: Integrated into a Practical Application
The claim recites additional elements as same as the claim 1, and therefore are interpreted as an abstract idea under the same premise as the claim 1.
Step 2B: Claim provides an Inventive Concept
The claim recites the limitations as same as the claim 1, and therefore is considered under the same premise as the claim 1 as no inventive concept in the claim, and thus it is ineligible.
As per claims 13-19, the claims are rejected under the same premises as the claims 2-8 respectively.
As per claim 20, the claim recites “A non-transitory computer-readable medium having machine-executable instructions stored thereon which, when executed by one or more processors of a computing system, cause the computing system to:” perform the similar limitations in the claim 1.
Step 1: Statutory Category
Claim 20 discloses a non-transitory computer-readable medium which is a manufacture within the meaning of the section.
Step 2A – Prong One: Judicial Exception Recited
The claim recites the limitations as same as the claim 1, and therefore are interpreted as an abstract idea under the same premise as the claim 1.
Step 2A – Prong Two: Integrated into a Practical Application
The claim recites additional elements as same as the claim 1, and therefore are interpreted as an abstract idea under the same premise as the claim 1.
Step 2B: Claim provides an Inventive Concept
The claim recites the limitations as same as the claim 1, and therefore is considered under the same premise as the claim 1 as no inventive concept in the claim, and thus it is ineligible.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the objection, and the rejections as set forth in this Office action.
Reasons for Allowance
The following is an examiner’s statement of reasons for allowance:
After conducting different searches in PE2E - SEARCH, Similarity and More Like Doc Searches, Google Scholar, and ACM Digital Library, it appears that none of prior arts discloses, teaches or fairly suggests the limitations as a whole in the independent claims 1, 12 and 20.
In particular, none of the prior arts, singular and any order combination, discloses the claimed limitations: “clustering the plurality of data records to generate a plurality of clusters of data records, based on the first set of attributes; obtaining, for each of the plurality of clusters, a corresponding second set of attributes; generating, for each of the plurality of clusters, a corresponding personality score vector, based on the second set of attributes; generating, by one or more LLM agents, a dynamic persona corresponding to at least one of the plurality of clusters, the dynamic persona including a third set of attributes”, as recited in the independent claims 1, 12 and 20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 12,430,346 B2 by Manning et al. teaches automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities
US 2023/0169103 A1 by Frazier et al. teaches enable effective and accurate modeling of a set of existing data profiles, perform categorization of the data profiles in an explainable way such that actions can be taken on the information to have predictable results. The systems and methods further facilitate means to categorize small text components, trained over dependent and independent model sets, to enable a cleaner and more explicit representation of information rich short-strings, in order to facilitate a more meaningful representation of the data profiles.
US 2025/0110970 A1 by Ganesan et al. teaches receiving, by a processor set, a plurality of tabular data records; generating, by the processor set, a plurality of clusters within the received plurality of tabular data records, wherein each cluster is associated with a specific real-world entity; identifying, by the processor set, informative features within a first cluster of the plurality of clusters of the received plurality of tabular data records; masking, by the processor set, a subset of the identified informative features within the first cluster of the plurality of clusters of the received plurality of tabular data records; and training, by the processor set, a tabular foundation model, using self-supervision techniques, based on the masked subset of the identified informative features within the first cluster of the plurality of clusters of the received plurality of tabular data records.
US 2014/0344270 A1 by Lambert et al. teaches data clustering and user modeling for next-best-action decisions. Specifically, a modeling tool is configured to: receive indicators within unstructured social data from a plurality of users; analyze the unstructured social data of each of the plurality of users to assign a set of feature vectors to each of the plurality of users, each feature vector corresponding to one or more personality characteristics of each of the plurality of users; and analyze the feature vectors to identify two or more users from the plurality of users sharing a set of similar feature vectors. The modeling tool is further configured to: group the two or more users from the plurality of users sharing the set of similar feature vectors to form a cluster; identify attributes of the cluster; and input the attributes of the cluster into a predictive model to determine an offer corresponding to the cluster.
GB 2572546 A by Nikhil et al. teaches creating entity records using a computer system comprises receiving unstructured data from existing data sources, e.g. the internet. The unstructured data is then structured to obtain data records comprising entity names and attributes, e.g. using a feature matrix. The data records are sorted into buckets based on similar entity attributes. The buckets are grouped into clusters using a pattern recognition algorithm, e.g. k-nearest neighbour algorithm. A prediction probability score, e.g. calculated with a random-forest classifier, is assigned to each of the data records within the clusters. Data records are then combined to form an entity record where the combined records comprise a prediction probability score higher than a predefined threshold. The method allows the automatic combination of data relating to the same person or entity into a single record, even in ambiguous situations, e.g. where two entities have the same name.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bai D. Vu whose telephone number is (571) 270-1751. The examiner can normally be reached 9:00 - 5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached at (571) 272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BAI D VU/Primary Examiner, Art Unit 2163 6/26/2026
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