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 .
Response to Arguments
2. Applicant's arguments with respect to claims 1-8 and 13-24 have been considered but are moot in view of the new ground(s) of rejection.
Information Disclosure Statement
3. The information disclosure statement (IDS) filed on 05/13/2026 and 05/12/2026 comply with the provisions of M.P.E.P. 609. The examiner has considered it.
Examiner's Note
4. "User segment" (According to paragraph 40 of the instant Specification): "As used
herein, a "user segment" refers to a group of users corresponding to a group of user profiles
and identified by a group of user identifiers. As used herein, a "user profile" refers to data corresponding to a user. Examples of data corresponding to a user include a name, contact
information, demographic data, user device information, a purchase history, a correspondence
history, and any other data relating to the user. As used herein, a "user identifier" refers to a
unique identifier (such as a name, an email address, an identification number, etc.) for a user. In
some cases, a user profile includes a user identifier. In some cases, the user segment includes
one or more users corresponding to user profiles that include a common attribute or quality."
"a label of the user segment" (According to paragraphs 90 and 117 of the instant Specification):
"machine learning model 220 generates a label of the user segment. In some examples, machine
learning model 220 identifies one or more attributes of the user segment, where the label is
based on the one or more attributes".
"summary statistics of the user segment" (According to paragraphs 27 and 50 of the instant
Specification): "using summary statistics and described traits of the user segment along with
projected performance of the user segment towards the content provider's objective".
A Large Language Model (According to Google): "A Large Language Model (LLM) is a type of
AI designed to understand, generate, and process human language by analyzing massive
datasets using deep learning, specifically neural networks called transformers. They predict the
next word in a sequence based on probability, allowing them to summarize, translate, and
answer questions. Common examples include ChatGPT, Gemini, and Claude."
Erlingsson et al, US 11,770,398, [Erlingsson: Column 101, lines 1-67 ("In some embodiments,
the one or more other natural language inputs for the other prompt may be generated according
to similar approaches as are described above, including machine learning approaches using a
trained model. In such embodiments, the natural language input and/or a response to the
received natural language input (e.g., including a response to the query corresponding to the
received natural language input) may be provided as input to such a model. ", i.e., 'generating a
prompt for a machine learning model')] [Erlingsson: Column 8, lines 31-43 ("a baseline of
datacenter activity can be modeled, and deviations from that baseline can be identified as
anomalous. Anomaly detection can be beneficial in a security context, a compliance context, an
asset management context, a DevOps context, and/or any other data analytics context", i.e.,
'contextual information')] [Erlingsson: Column 86, lines 18-43 ("For example, historical
information may be used to compare the current state of a particular cluster (as measured by
one or more quantifiable characteristics associated with the cluster) with a previous state of the
cluster such that trends and/or trajectories may be identified.' i.e., 'data trend')] [Erlingsson:
Column 101, lines 1-10 and column 101, lines 43-67 ("the corresponding query may be
generated from the received natural language input using machine learning approaches", i.e.,
'natural language text input' and 'machine learning model')] [Erlingsson: Column 17, lines 31-
44 ("automatically discover entities (which may implement compute assets 16) deployed in a
given datacenter. Examples of entities include workloads, applications, processes, machines,
virtual machines, containers, files, IP addresses, domain names, and users. The entities may be
grouped together logically (into analysis groups) based on behaviors, and temporal behavior
baselines can be established", i.e., 'user segment')] [Erlingsson: Column 42, lines 58-67
through column 43, lines 1-5 ("t 363, the received network activity is used to identify user login activity. And, at 364, a logical graph that links the user login activity to at least one user and at
least one process is generated", i.e., 'user segment')] [Erlingsson: Column 49, lines 34-45 ("the
effective user in the Tier 2 node may or may not match the original user (while the original user
in the Tier 2 node will match the original user in the Tier 1 node)", i.e., 'user segment')]
[Erlingsson: Column 54, lines 32-47 ("As one example, user A can see that contact was made
with examplebad.com a total of 17 times during the time period", i.e., 'user segment')]
[Erlingsson: Column 75, lines 30-43 "customers (and their corresponding deployments) may be
modeled into logical groups such that cross customer learning could be carried out only across
customers in the same logical group, or other customers in the same logical group may be given
a greater weighting for the purposes of cross customer learning"., i.e., 'user segment')]
[Erlingsson: Column 5, lines 45-50 and column 25, lines 31-39 ("Such queries may be
generated using any suitable query language" and "using a query language, such as SQL, ".,
'structured query ')].
Sadr et al, US 20240202796, [Sadr: Abstract and paragraphs 2, 7, 10 and 48 ("to generate a
prompt input that can be processed by a machine-learned model to generate outputs that can be
reviewed by a user and selected to be input into a search engine to receive search results
associated with the selected output")] [Sadr: Paragraphs 80 and 107 ("the images can provide
a more detailed context of what a user is requesting during the search, which can allow for a
more tailored search than text alone" and "learned information 402 (e.g., fashion knowledge,
personalization (e.g., based on stored data associated with a user), and/or trends (e.g., purchase
trends, social media trends, and/or search trends)) can be obtained and utilized to generate a prompt and/or to suggest prompt inputs for selection via selectable user interface elements", i.e.,
'contextual information comprising a data trend')].
Divakaran et al, US 20210297498, [Divakaran: Paragraphs 10 and 37 ("creating a respective
first modality vector representation of the content of the multimodal content having a first
modality using a machine learning model for each of a plurality of content of the multimodal
content, at least a second embedding module creating at least a respective second modality
vector representation of content of the multimodal content having at least a second modality
using a machine learning model for each of a plurality of content of the multimodal content")].
Tran, US 20230351102, [Tran: Abstract and paragraph 9 ("generate a document from one or
more first and second text prompts, generating one or more context-sensitive text suggestions
using a transformer with an encoder on the text prompts and a decoder that produces a text
expansion to provide the context-sensitive text suggestions based on the one or more first and
second text prompts by applying generative artificial intelligence", i.e., 'generating a prompt for
a machine learning model')] [Tran: Paragraph 198 ("determined by the learning machine
trained for routing users to agents includes rating agents on performance or success of agent
data and caller data, or both. The checking for optimal interaction includes combining agent
work performance, agent demographic/psychographic data, and other work performance data
("agent data"), along with demographic, psychographic, and other business-relevant data about
callers ("caller data"). Agent and caller demographic data can be: gender, race, age, education,
accent, income, nationality, ethnicity, area code, zip code, marital status, job status, credit score, for example. Agent and caller psychographic data can cover introversion, sociability,
work/employment status, film and television preferences, among others", i.e., 'user segment')].
Plotkin, US 20260178828, [Plotkin: Paragraph 201 (“The term “initial” is used in connection with the initial text 118 because, if the language model 112 is a generative language model, then providing the initial text 118 to the language model 112 may result in the language model 112 treating the initial text 118 as a prompt and producing output which continues the prompt. (In fact that initial text 118 sometimes is referred to as a “prompt.”) “)] [Plotkin: Paragraph 44 (“Converting input unstructured text into output structured text, wherein at least some of the output structured text has the same meaning as at least some of the input unstructured text “)] [Plotkin: Paragraph 116 (“The user input 104 may, for example, consist of unstructured text, such as a single sentence or a plurality of consecutive sentences. Text in the user input 104 may, for example, be written in a natural language (e.g., English)”)] [Plotkin: Paragraph 609 (“extract contextual relationships within patent documents, and continuously enhance understanding over time based on past interactions”)] [Plotkin: Paragraph 612 (“Humans leverage these innate skills to interpret patent language, draw conceptual connections, make contextual inferences, and render informed opinions.”)] [Plotkin: Paragraph 587 (“Some existing technologies perform clustering of patent documents for purposes such as identifying trends in patent filings and grants, such as identifying technology fields which are densely (and sparsely) populated with patent documents.”)] [Plotkin: Paragraph 502 (“By incorporating optimization processes into a patent landscaping or technology scouting system, stakeholders may identify trends, emerging technologies, and potential collaboration opportunities.”)] [Plotkin: Paragraph 195 (“The language model application 110 may perform a compound transformation on some or all of the user input 104 to generate the initial text 118, where at least one of the component transformations of the compound transformation produces output which includes or consists of non-freeform data (e.g., structured text and/or computer-encoded data). As one example, the compound transformation may include: (1) a first transformation which generates first non-freeform data (e.g., structured text and/or computer-encoded data) based on some or all of the user input”, i.e., transforming unstructured data into structured data)] [Plotkin: Paragraph 203 (“The user input 104 may include data (referred to herein as “referent identification data” for ease of explanation) which represents or otherwise identifies a plurality of referents. Examples of such a plurality of referents include a plurality of patents, patent claims, patent claim elements, patent claim limitations, products, product categories, product manufacturers, and product industries”, i.e., ‘user segment by identifying a set of user indentifiers’)].
Gittino et al, US 20240420149, [Gittino: Paragraph 45 (“That is, the machine learning system may receive inputs of data related to the users, subsequent actions of the users, or other inputs to find subtle patterns, connections, correlations, or trends that indicate that users belong in a configured community even though the users may not have immediately observable connections to the community”)].
Claim Rejections - 35 USC § 102
5. 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 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.
6. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
7. Claims 1-4, 6-8, 13, 15-17 and 19-24 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Crabtree et al (US 20210019674).
Claim 1:
Crabtree suggests a method for data clustering, comprising: obtaining contextual information comprising a data trend and a natural language text input corresponding to the data trend [Crabtree: Paragraph 135 (“According to the aspect, behavior analytics may utilize passive information feeds from a plurality of existing endpoints (for example, including but not limited to user activity on a network, network performance, or device behavior) to generate security solutions. In an initial step 801, a web crawler 115 may passively collect activity information, which may then be processed 802 using a DCG 155 to analyze behavior patterns. Based on this initial analysis, anomalous behavior may be recognized 803 (for example, based on a threshold of variance from an established pattern or trend) such as high-risk users or malicious software operators such as bots”)] [Crabtree: Paragraph 51 (“As used herein, semantic computing is the use of natural language processing (NLP) and machine learning to derive context and meaning from the search before converting the search into an SQL query that is then used to parse and update ontological databases within the system”)]. Crabtree suggests generating a prompt for a machine learning model based on the contextual information and the natural language text input, wherein the prompt includes the contextual information and the natural language text input [Crabtree: Paragraph 51 (“As used herein, semantic computing is the use of natural language processing (NLP) and machine learning to derive context and meaning from the search before converting the search into an SQL query that is then used to parse and update ontological databases within the system”)]. Crabtree suggests generating, using the machine learning model, a structured query for a database of users in response to the machine learning model receiving the prompt as an input [Crabtree: Paragraph 51 (“As used herein, semantic computing is the use of natural language processing (NLP) and machine learning to derive context and meaning from the search before converting the search into an SQL query that is then used to parse and update ontological databases within the system”)]. Crabtree suggests generating, using a user experience platform, a user segment by identifying a set of user identifiers based on the structured queryand including a corresponding set of the users in the user segment in response to the identification [Crabtree: Abstract and paragraph 6 (“allow a user to query an individual or business and returns a profile and a rating associated with the risk of that entity. The profile consists of an advanced temporospatial weighted and directional knowledge graph that is generated by ingesting, processing, and transforming a vast amount of complex data for the purpose of human comprehension and further system analysis”, i.e., returning ‘user segment’ data; “risk profiling” = ‘user identifiers’ or ‘identification’)] [Crabtree: Paragraph 67 (“James R and Thomas G” = ‘user identifiers’ or ‘identification’)].
Claim 2:
Crabtree suggests monitoring, using the user experience platform, a set of data [Crabtree: Paragraphs 74, 85 and 94 (“modify its behavior or understanding without being explicitly programmed to do so” and “Customer-specific profiling” and “system user behavior analytics”)]; and detecting, using the user experience platform, the data trend in the set of data [Crabtree: Paragraph 135 (“According to the aspect, behavior analytics may utilize passive information feeds from a plurality of existing endpoints (for example, including but not limited to user activity on a network, network performance, or device behavior) to generate security solutions. In an initial step 801, a web crawler 115 may passively collect activity information, which may then be processed 802 using a DCG 155 to analyze behavior patterns. Based on this initial analysis, anomalous behavior may be recognized 803 (for example, based on a threshold of variance from an established pattern or trend) such as high-risk users or malicious software operators such as bots”)].
Claim 3:
Crabtree suggests generating, using the machine learning model, a label of the user segment [Crabtree: Paragraph 6 (“and topics associated with the subject”)] [Crabtree: Paragraphs 67-68, 147 and 149 (“a relationship between two nodes that forms an edge can be qualified using a “label.””)].
Claim 4:
Crabtree suggests identifying, using the machine learning model, one or more attributes of the user segment, wherein the label is based on the one or more attributes. [Crabtree: Paragraph 6 (“and topics associated with the subject”)] [Crabtree: Paragraphs 67-68, 147 and 149 (“a relationship between two nodes that forms an edge can be qualified using a “label.””)].
Claim 6:
Crabtree suggests receiving, by the machine learning model, an additional prompt; and
modifying, using the user experience platform, the user segment based on the additional prompt [Crabtree: Paragraph 135 (“According to the aspect, behavior analytics may utilize passive information feeds from a plurality of existing endpoints (for example, including but not limited to user activity on a network, network performance, or device behavior) to generate security solutions. In an initial step 801, a web crawler 115 may passively collect activity information, which may then be processed 802 using a DCG 155 to analyze behavior patterns. Based on this initial analysis, anomalous behavior may be recognized 803 (for example, based on a threshold of variance from an established pattern or trend) such as high-risk users or malicious software operators such as bots”)] [Crabtree: Paragraph 51 (“As used herein, semantic computing is the use of natural language processing (NLP) and machine learning to derive context and meaning from the search before converting the search into an SQL query that is then used to parse and update ontological databases within the system”)].
Claim 7:
Crabtree suggests receiving, via a user interface, a query about the user segment; and
generating a response to the query using the machine learning mode [Crabtree: Paragraph 135 (“According to the aspect, behavior analytics may utilize passive information feeds from a plurality of existing endpoints (for example, including but not limited to user activity on a network, network performance, or device behavior) to generate security solutions. In an initial step 801, a web crawler 115 may passively collect activity information, which may then be processed 802 using a DCG 155 to analyze behavior patterns. Based on this initial analysis, anomalous behavior may be recognized 803 (for example, based on a threshold of variance from an established pattern or trend) such as high-risk users or malicious software operators such as bots”)] [Crabtree: Paragraph 51 (“As used herein, semantic computing is the use of natural language processing (NLP) and machine learning to derive context and meaning from the search before converting the search into an SQL query that is then used to parse and update ontological databases within the system”)].
Claim 8:
Crabtree suggests generating, using the machine learning model, a behavioral prediction for the user segment [Crabtree: Paragraphs 38, 57 and 79 (“might predict the user's intent in response to an ambiguous search query” and “for predictive risk analysis” and “based predictive statistics functions and machine learning algorithms to allow future trends and outcomes”)].
Claim 13:
Claim 13 is essentially the same as claim 1 except that it sets forth the claimed invention as an apparatus rather than a method and rejected under the same reasons as applied above.
Claim 15:
Crabtree suggests wherein: the machine learning model comprises a transformer [Crabtree: Paragraphs 50, 68 and 69 (“and transforming a vast amount of complex data for the purpose of human comprehension and further system analysis”)].
Claim 16:
Crabtree suggests wherein: the machine learning model is further trained to generate a label of the user segment [Crabtree: Paragraph 6 (“and topics associated with the subject”)] [Crabtree: Paragraphs 67-68, 147 and 149 (“a relationship between two nodes that forms an edge can be qualified using a “label.””)].
Claim 17:
Crabtree suggests wherein: the label is based on one or more attributes of the user segment [Crabtree: Paragraph 6 (“and topics associated with the subject”)] [Crabtree: Paragraphs 67-68, 147 and 149 (“a relationship between two nodes that forms an edge can be qualified using a “label.””)].
Claim 19:
Claim 19 is essentially the same as claim 6 except that it sets forth the claimed invention as an apparatus rather than a method and rejected under the same reasons as applied above.
Claim 20:
Claim 20 is essentially the same as claim 13 except that it sets forth the claimed invention as an apparatus rather than a method and rejected under the same reasons as applied above.
Claim 21:
Claim 21 is essentially the same as claim 1 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above.
Claim 22:
Claim 22 is essentially the same as claim 2 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above.
Claim 23:
Claim 23 is essentially the same as claim 3 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above.
Claim 24:
Claim 24 is essentially the same as claim 4 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above.
Claim Rejections - 35 USC § 103
8. 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 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.
9. 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.
10. Claims 5 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Crabtree et al (US 20210019674), in view of Fusting et al (US 20240177068).
Claim 5:
The combined teachings of Crabtree and Fusting suggest generating, using the machine learning model, one or more summary statistics of the user segment [Fusting: Paragraph 7 (“the predicted distribution of values for each user attribute comprises a distribution with predicted summary statistics. In some embodiments, the uncertainty measure for the plurality of machine learning models comprises (i) a variance of predicted means from each machine learning model for the given user attribute and (ii) a mean of predicted standard deviations from each machine learning model for the given user attribute. In some embodiments, training each of a plurality of machine learning models using historical user profile data to predict a distribution of values for each of a plurality of user attributes comprises training each machine learning model on a different subset of the historical user profile data”)].
Both references (Crabtree and Fusting) taught features that were directed to analogous art and they were directed to the same field of endeavor, such as data processing. It would have been obvious to one of ordinary skill in the art at the time the invention was made, having the teachings of Crabtree and Fusting before him/her, to modify the system of Crabtree with the teaching of Fusting in order to generate an overview of user data [Fusting: Paragraph 5].
Claim 18:
Claim 18 is essentially the same as claim 5 except that it sets forth the claimed invention as an apparatus rather than a method and rejected under the same reasons as applied above.
11. Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Crabtree et al (US 20210019674), in view of Dobson et al (US 20230410223).
Claim 14:
The combined teachings of Crabtree and Dobson suggest wherein: the machine learning model comprises a large language model [Dobson: Paragraph 91 (“Generative AI with large language models (LLMs) are integrated, such as, for example and not limitation Open AI's ChatGPT”)].
Both references (Crabtree and Dobson) taught features that were directed to analogous art and they were directed to the same field of endeavor, such as data processing. It would have been obvious to one of ordinary skill in the art at the time the invention was made, having the teachings of Crabtree and Dobson before him/her, to modify the system of Crabtree with the teaching of Dobson in order to implement a large language model [Dobson: Paragraph 91].
Conclusion
12. 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 extension fee 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 date of this final action.
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to [Hung D. Le], whose telephone number is [571-270-1404]. The examiner can normally be communicated on [Monday to Friday: 9:00 A.M. to 5:00 P.M.].
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on [571-272-4080]. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, contact [800-786-9199 (IN USA OR CANADA) or 571-272-1000].
Hung Le
07/16/2026
/HUNG D LE/Primary Examiner, Art Unit 2161
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