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 § 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:
Claims 1, 8 and 15 are directed to an abstract idea because (at Prong 1) it recites collecting conversation text and user data, analyzing/classifying that information (tagging, classifying, summarizing), and using a model to match the user to others and generate a response from stored historical information. Under current USPTO subject-matter eligibility framing, this fits within the recognized abstract-idea groupings—especially mental processes (categorizing/tagging/summarizing information) and mathematical concepts (statistical model), and it resemble information-processing/organizing-human-interaction concepts.
The claim (at Prong 2A) does not clearly integrate that judicial exception into a practical application in the eligibility sense, because the “additional elements” are largely generic: a device/user device, a chatbot interface, an LLM, a statistical model, and converting results into a “searchable document.” The claim does not recite a specific technical improvement to computer functionality. Instead, it broadly uses known computer/AI components as tools to perform and present the underlying information-analysis and recommendation task.
The claims (at Prong 2B), as written, amounts to “collecting information, analyzing it, and presenting results” using generic computing/AI components, which courts have repeatedly found ineligible when not tied to a specific technological improvement. The Federal Circuit’s Electric Power Group line of cases is cited for treating such data-collection/analysis/display workflows as abstract when the computer is merely a tool, and more recent decisions continue to apply similar reasoning to software claims that process and present information without a concrete improvement to computer technology itself.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims are (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. There is further no improvement to the computing device.
Dependent claims 2-7, 9-14 and 16-20 are further recite an abstract idea performable by a human and do not amount to significantly more than the abstract idea as they do not provide steps other than what is conventionally known in data collection and management.
Claim 2: directed to the abstract idea of identifying and performing an "action," which is a fundamental business practice or human activity that does not require a technical solution. The limitation merely appends generic computational results to a high-level outcome without specifying a technical implementation.
Claims 3 and 16: Providing "engagement options" is a method of organizing human activity that does not transcend the abstract idea of communication. The computer acts only as a generic tool to present choices derived from the underlying abstract data analysis.
Claims 4 and 17: Determining and applying "modifications" to a conversation is a functional result that mirrors human conversational adjustment. It lacks an inventive concept because it does not describe a technical improvement to the chatbot's underlying software or hardware architecture.
Claim 5: Reciting a "k-means clustering model" is a mathematical algorithm, which is a recognized category of abstract ideas. Applying well-known mathematical techniques to data analysis on a generic computer does not satisfy the requirements for patent eligibility.
Claim 6: claim simply describes a conversational outcome (a "modification") which is a high-level functional result of the abstract data processing. It fails to recite a specific technical mechanism that improves the chatbot beyond the abstract idea of producing a response.
Claim 7: Maintaining "user engagement" is a method of organizing human activity rather than a technical solution to a technical problem. The limitation is purely result-oriented and does not provide an inventive step in the field of computer science.
Claims 9 and 18: quantitative analysis is a mental process that humans can perform and does not offer "significantly more" when performed by a processor.
Claims 10 and 19: Receiving "feedback" and "updating data" are routine, conventional activities for data management and learning systems. These steps are considered well-understood and do not transform the abstract idea into a patent-eligible invention.
Claims 11 and 20: The claim does not specify a technical improvement in how trends are identified, merely reciting the abstract concept of trend-based response generation.
Claim 12: Utilizing a computer to perform this prediction is a generic application of data analysis that remains directed to an abstract idea.
Claim 13: Monitoring a user's choice to switch communication channels does not provide a technical improvement to the computer's operation.
Claim 14: These are generic computer functions that facilitate the abstract idea of data retrieval and do not constitute an inventive concept.
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.
Claims 1-4, 6-12 and 14-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Abramson et al. (US 10,853,717).
Claim 1,
Abramson teaches a method, comprising: providing, by a device, a chatbot interface to a user via a user device ([col. 17 line 66 to col. 8 line 1] receiving, by the trained chat bot, dialogue from a user via an interface accessible to the computer-readable storage device);
receiving, by the device, text data associated with a conversation of the user via the chatbot interface ([col. 4 lines 30-32] receiving, by the trained chat bot, dialogue from a user via an interface; receive input via a user interface component; examples of input include text input);
processing, by the device, the text data, with one or more large language models, to generate conversation tags representative of content of the conversation ([col. 2 lines 55-58] [col. 6 lines 47-48] a language model to converse where the model maybe a predictive or statistical language model; processing the personalized data may comprise identifying and categorizing conversation data);
generating, by the device, user attribute tags based on user data identifying activity and a profile of the user ([col. 2 lines 40-44] [col. 17 lines 10-41] social data is further based on user profile information, behavioral data, transactional data and geolocation data; similarity characteristics include demographic data and behavioral data);
classifying, by the device, the conversation tags and the user attribute tags to generate classified tags ([col. 7 lines 15-17] the scoring or comparison algorithm/model may generate and/or assign scores or labels to the evaluated characteristics);
converting, by the device, the text data and the classified tags to a searchable document with a summary and the classified tags ([Abstract] [col. 4 lines 48-64] [col. 9 lines 65-67] special index/chat index: create or modify a special index; creating/modifying a personalized chat index; the personalized personality index may comprise processed data including image tags and descriptions; ML may detect tags and summarize events);
processing, by the device, the searchable document and historical tag data, with a statistical model, to identify a plurality of users that match the user based on the historical tag data and the classified tags of the searchable document ([col. 6 lines 25-60] organizing/storing by topical/type criteria: data store may store by social data subject/topic and social data type; finding similar people/entities: determining similarities between a specific person/entity and another person/entity (e.g., the “other person”) may include using machine learned techniques and/or natural language processing techniques to analyze and compare the social data of the other person);
determining, by the device, degrees of match between the plurality of users and the user based on the historical tag data and the classified tags of the searchable document ([col. 7 lines 17-21] the scoring or comparison algorithm/model may use the generated scores/labels to determine a similarity score or metric for the other person; the similarity score/metric may represent the estimated similarity between a specific person/entity and the other person/entity);
identifying, by the device, one of the plurality of users based on the degrees of match ([col. 17 lines 31-41] identifying similar entities: identifying one or more entities similar to the specific entity and determining similarities between the one or more entities and the specific entity (at least one similar entity is identified based on the similarity determination/metric));
utilizing, by the device, the historical tag data associated with the one of the plurality of users to generate a response for the user ([col. 8 lines 29-48] [col. 16 lines 62-67] response generation using data sources in preferred order including social data from users similar to the specific person/entity; hierarchical traversal; utilizing a hierarchical data traversal process to collect response data from one or more data sources accessible to the personality index; the hierarchical data traversal process comprises evaluating social data from the specific entity, evaluating social data from entities similar to the specific entity, evaluating social data from a global user base, and evaluating generic response options); and
providing, by the device, the response to the user via the chatbot interface and the user device ([col. 16 line 60 to col. 17 line 9] generating, by the chat bot, a response and providing, to a user interaction with the chat bot, the questions; generating a response to the received dialogue).
Claim 2,
Abramson further teaches the method of claim 1, further comprising: utilizing the historical tag data associated with the one of the plurality of users to identify an action to be performed for the user ([col. 4 lines 48-64] [col. 17 lines 3-9] tag processed data: processed data.. image tags and descriptions and may change or evolve over time; action identification based on index contents: determining the personality index does not comprise data for addressing one or more parts of the submitted dialogue; composing, by the chat bot, one or more questions to address the data not comprised in the personality index); and
causing the action to be performed for the user ([col. 17 lines 3-9] posing, to a user interacting with the chat bot, the one or more questions; providing, to a user interacting with the chat bot, the questions).
Claim 3,
Abramson further teaches the method of claim 2, wherein causing the action to be performed comprises: causing engagement options to be provided to the user via the chatbot interface ([Fig. 2] [col 5 lines 25-26] scripted and/or pre-generated automated questions/replies via chat bot engine).
Claim 4,
Abramson further teaches the method of claim 2, wherein causing the action to be performed comprises: determining one or more modifications for the conversation ([col. 17 lines 6-9] composing, by the chat bot, one or more questions to address the data not comprised in the personality index); and
applying the one or more modifications to the conversation via the chatbot interface ([col. 17 lines 6-9] posing, to a user interacting with the chat bot, the one or more questions).
Claim 6,
Abramson further teaches the method of claim 1, wherein the response includes a modification of the conversation of the user via the chatbot interface ([col. 16 line 59 to col. 17 line 9] conversational modification via questions: response comprises composing questions; providing the questions; posing the questions).
Claim 7,
Abramson further teaches the method of claim 1, wherein the response maintains engagement of the user with the chatbot interface ([col. 11 lines 45-46] provide a more immersive user experience for user interacting with the chat bot/LU model).
Claim 8,
A device, comprising: one or more processors configured to: receive, from a user device associated with a user, text data associated with a conversation of the user, wherein the text data is received via a chatbot interface provided to the user device; process the text data, with one or more large language models, to generate conversation tags representative of content of the conversation; generate user attribute tags based on user data identifying activity and a profile of the user; classify the conversation tags and the user attribute tags to generate classified tags; convert the text data and the classified tags to a searchable document with a summary and the classified tags; process the searchable document and historical tag data, with a statistical model, to identify a plurality of users that match the user based on the historical tag data and the classified tags of the searchable document; determine degrees of match between the plurality of users and the user based on the historical tag data and the classified tags of the searchable document; identify one of the plurality of users based on the degrees of match; utilize the historical tag data associated with the one of the plurality of users to generate a response for the user; and provide the response to the user via the chatbot interface and the user device. (Claim 8 contains subject matter similar to claim 1, and thus is rejected under similar rationale. In addition, claim 8 requires a processor which is taught by Abramson Fig. 4)
Claim 9,
Abramson further teaches the device of claim 8, wherein the one or more processors, to determine the degrees of match between the plurality of users and the user based on the historical tag data and the classified tags of the searchable document, are configured to: determine the degrees of match between the plurality of users and the user based on a quantity of tags of the historical tag data that match the classified tags of the searchable document ([col. 6 line 52 to col. 7 line 24] assigning score or labels and using them to compute a similarity metric: scores or labels may be assigned and use the generated scores/labels to determine a similarity score or metric; similarity computation using set overlap metrics (Jaccard similarity)).
Claim 10,
Abramson further teaches the device of claim 8, wherein the one or more processors are further configured to: receive feedback associated with providing the response to the user via the chatbot interface and the user device ([col. 6 line 52 to col. 7 line 24] feedback/approval indicators: approval indictors… likes/dislikes, rating, reviews, comments, etc.); and
update the historical tag data based on the feedback ([col. 6 line 52 to col. 7 line 24] the processed personalized data may be used to create, organize, populate or update a personalized personality index for the specific person/entity identified in the request).
Claim 11,
Abramson further teaches the device of claim 8, wherein the one or more processors, to utilize the historical tag data associated with the one of the plurality of users to generate the response for the user, are configured to: identify one or more trends in the historical tag data associated with the one of the plurality of users ([col. 6 line 52 to col. 7 line 24] behavioral data (e.g. access dates/times, transaction trends, purchase history, frequented sites, dwell times, click data, etc.), psychographic data (e.g., user interests, opinions, likes/dislikes, values, attitudes, habits, etc.)); and
generate the response for the user based on the one or more trends ([col. 8 lines 13-48] to provide a response using data).
Claim 12,
Abramson further teaches the device of claim 8, wherein the one or more processors, to utilize the historical tag data associated with the one of the plurality of users to generate the response for the user, are configured to: utilize the historical tag data associated with the one of the plurality of users to predict a subject of interest for the user; and generate the response for the user based on the subject of interest ([col. 11 lines 10-46] to interact conversationally in the personality of a specific person/entity associated with the personalized personality index; interacting conversationally may include determining the a subject and/or intent for one or more expressions of a dialogue, identifying a data source comprising response data, determining whether response data is present in accessible data sources, generating and posing questions to supplement gaps and/or verify data in the data source data, etc.).
Claim 14,
Abramson further teaches the device of claim 8, wherein the one or more processors are further configured to: generate the historical tag data based on historical conversations associated with one or more user devices and the chatbot interface ([col. 17 lines 10-41] accessing social data comprising conversational data and using the social data to create a personality index); and store the historical tag data in a data structure (Fig. 4] data structure).
Claim 15,
A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to: provide a chatbot interface to a user via a user device; receive text data associated with a conversation of the user via the chatbot interface; process the text data, with one or more large language models, to generate conversation tags representative of content of the conversation; generate user attribute tags based on user data identifying activity and a profile of the user; classify the conversation tags and the user attribute tags to generate classified tags; process the classified tags and historical tag data, with a statistical model, to identify a plurality of users that match the user; determine degrees of match between the plurality of users and the user based on the historical tag data and the classified tags; identify one of the plurality of users based on the degrees of match; utilize the historical tag data associated with the one of the plurality of users to identify an action to be performed for the user; and cause the action to be performed for the user. (Claim 15 contains subject matter similar to claim 1, and thus is rejected under similar rationale. Claim 15 requires a processor which is taught by Abramson in Fig. 4)
Claim 16,
The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, that cause the device to cause the action to be performed, cause the device to: cause engagement options to be provided to the user via the chatbot interface. (Claim 16 contains subject matter similar to claim 3, and thus is rejected under similar rationale)
Claim 17,
The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, that cause the device to cause the action to be performed, cause the device to: determine one or more modifications for the conversation; and apply the one or more modifications to the conversation via the chatbot interface. (Claim 17 contains subject matter similar to claim 4, and thus is rejected under similar rationale)
Claim 18,
The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, that cause the device to determine the degrees of match between the plurality of users and the user based on the historical tag data and the classified tags, cause the device to: determine the degrees of match between the plurality of users and the user based on a quantity of tags of the historical tag data that match the classified tags. (Claim 18 contains subject matter similar to claim 9, and thus is rejected under similar rationale)
Claim 19,
The non-transitory computer-readable medium of claim 15, wherein the one or more instructions further cause the device to: receive feedback associated with causing the action to be performed for the user; and update the historical tag data based on the feedback. (Claim 19 contains subject matter similar to claim 10, and thus is rejected under similar rationale)
Claim 20,
The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, that cause the device to utilize the historical tag data associated with the one of the plurality of users to identify the action to be performed for the user, cause the device to: identify one or more trends in the historical tag data associated with the one of the plurality of users; and generate the action to be performed for the user based on the one or more trends. (Claim 20 contains subject matter similar to claim 11, and thus is rejected under similar rationale)
Claim Rejections - 35 USC § 103
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.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Abramson et al. (US 10,853,717) and further in view of Rajagopal et al. (US 2021/0064826).
Claim 5,
Abramson teaches all the limitations in claim 1. The difference between the prior art and the claimed invention is that Abramson does not explicitly teach wherein the statistical model is a k-means clustering model.
Rajagopal teaches wherein the statistical model is a k-means clustering model ([Fig. 4A] [0073] the historical chat mining method may include a K-mean cluster).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Abramson with teachings of Rajagopal by modifying the creating a conversational chat bot of a specific person as taught by Abramson to include wherein the statistical model is a k-means clustering model as taught by Rajagopal for the benefit of generating a set of utterances based on a historical chat log (Rajagopal [0073]).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Abramson et al. (US 10,853,717) and further in view of Beechuk et al. (US 10,311,364).
Claim 13,
Abramson teaches all the limitations in claim 8. The difference between the prior art and the claimed invention is that Abramson does not explicitly tech wherein the one or more processors are further configured to: determine whether the user has escalated the conversation to a live agent based on the response; and evaluate an effectiveness of the response based on whether the user escalates the conversation to the live agent.
Beechuk teaches wherein the one or more processors are further configured to: determine whether the user has escalated the conversation to a live agent based on the response; and evaluate an effectiveness of the response based on whether the user escalates the conversation to the live agent ([Fig. 10] [col. 5 line 65 to col. 6 line 8] automatic severity detection and possible pre-escalation of response based on the detected severity of the case; routing of case to appropriate parties; recommendation of solutions from detected similar cases; and identification of experts to assist).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Abramson with teachings of Beechuk by modifying the creating a conversational chat bot of a specific person as taught by Abramson to include wherein the one or more processors are further configured to: determine whether the user has escalated the conversation to a live agent based on the response; and evaluate an effectiveness of the response based on whether the user escalates the conversation to the live agent as taught by Beechuk for the benefit of predicting actions (Beechuk [Abstract]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Greene et al. (US 11,386,153) – A flexible tagging and searching system can be configured to associate tags with the input data to enable a search of the input data. The flexible tagging and searching system can receive user data and determine an identifier associated with the user data. The system can compare the identifier with other identifiers to determine that the identifier is unique. After determining that the identifier is unique, the system can associate a critical tag with the user data and store the critical tag in a database. Then the system can associate a non-critical tag with the user data. In some instances, the system can return query results such as a set of tag data and/or a set of data items.
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SHREYANS A. PATEL
Primary Examiner
Art Unit 2653
/SHREYANS A PATEL/ Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/ Supervisory Patent Examiner, Art Unit 2659