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 .
Status of the Claims
The Amendment filed on 10/20/2025 has been entered. Claims 1-14 are pending in the instant patent application.
Response to Claim Amendments
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §101 rejections. The rejections remain pending per guidelines for 101 analysis (PEG 2019).
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §103 rejections. The rejections remain pending.
Response to 35 U.S.C. §101 Arguments
Applicant’s arguments regarding 35 U.S.C. §101 rejection of the claims have been fully considered, but are not persuasive.
Regarding Applicant’s assertion that the current claims do not recite abstract ideas (Mental Processes), Examiner respectfully disagrees. Examiner respectfully reminds Applicant, general purpose computer elements/structure, similar to the claimed invention, used to apply a judicial exception, by use of instruction implemented on a computer, has not been found by the courts to integrate the abstract idea into a practical application; see MPEP 2106.05(f). Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Examiner will further note an important consideration to evaluate when determining whether the claim as a whole integrates a judicial exception into a practical application is whether the claimed invention improves the functioning of a computer or other technology. MPEP 2106.04(a) and 2106.05(a) provide a detailed explanation of how to perform this analysis. In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. In analyzing the specification, Examiner maintains that the specification sets forth an improvement, but in a conclusory manner and furthermore the claims do not reflect the disclosed improvement or effectively demonstrate an improvement to existing technology. In addition, (ref: Oct 2019 Update: Subject Matter Eligibility).
As noted in Ex Parte Desjardins, the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. There were clear improvements noted and further reflected in the claim language. The same cannot be said of the current claims in light of Ex Parte Desjardins. Thus for in at least these reasons, Examiner maintains that the claim language recites abstract ideas.
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.
Regarding Claims are 1-7, they are directed to a method, however the claims directed to a judicial exception without significantly more. Claims 1-7 are directed to the abstract idea of revising a score associated with an interaction.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites traversing a score revision decision tree to categorize the interaction, based on interaction data and interaction feedback data associated with the interaction; and selecting, based on the categorization of the interaction, an indication of a probability that the score associated with the interaction should be revised; wherein interaction data comprises data extracted from the interaction; interaction feedback data comprises data extracted from feedback about the interaction; and the score revision decision tree comprises a decision tree data structure comprising at least one decision node, each decision node corresponding to at least one data point of the interaction data or interaction feedback data.
These claim limitations fall within the Mental Processes grouping of abstract ideas because each limitation can be practically be performed by a human in the human mind and/or with pen/paper (including an observation, evaluation, judgment, opinion).
Accordingly, the claim recites an abstract idea and dependent claims 2-7 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a computer processor. The computer processor is merely a generic computing device and does not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 1 includes various elements that are not directed to the abstract idea under 2A. These elements include a computer processor and the generic computing elements described in the Applicant's specification in at least Para 0043-0052. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 1 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 8-14, they are directed to a system, however the claims are directed to a judicial exception without significantly more. Claims 8-14 are directed to the abstract idea of handling a revision of a score of an interaction.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 8, claim 8 recites a score revision decision tree; traverse the score revision decision tree to categorize the interaction, based on interaction data and interaction feedback data associated with the interaction; and select, based on the categorization of the interaction, an indication of a probability that the score associated with the interaction should be revised; wherein interaction data comprises data extracted from the interaction; interaction feedback data comprises data extracted from feedback about the interaction; and the score revision decision tree comprises a decision tree data structure comprising at least one decision node, each decision node corresponding to at least one data point of the interaction data or interaction feedback data.
These claim limitations fall within the Mental Processes grouping of abstract ideas because each limitation can be practically be performed by a human in the human mind and/or with pen/paper (including an observation, evaluation, judgment, opinion).
Accordingly, the claim recites an abstract idea and dependent claims 9-14 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a memory and at least one processor. The memory and at least one processor are generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 8-10 and 13-14 includes various elements that are not directed to the abstract idea under 2A. These elements include a memory, at least one processor, an output device and the generic computing elements described in the Applicant's specification in at least Para 0043- 0052. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claims 8-10 and 13-14, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Response to 35 U.S.C. §103 Arguments
Applicant’s arguments regarding 35 U.S.C. §103 rejection of the claims have been fully considered, but are not persuasive.
Regarding Applicant’s argument that the cited art does not teach the limitations, Examiner respectfully disagrees. Taking the claim language under its broadest reasonable interpretation, Examiner maintains that the cited art teaches the limitations as explicitly written. If, however, Applicant cites to claim explicit limitations, Examiner suggests that it be explicitly stated in the claim language and not resorted to what is stated in the specification. Examiner will further note that graph databases consist of nodes/entities that depict connected data.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-6, 8 and 10-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Olmstead et al. (US 2018/0082678 A1) in view of Abitbol et al. (US 2021/0004885 A1).
Regarding Claim 1, Olmstead teaches the limitations of Claim 1 which states
traversing a score revision decision tree to categorize the interaction, based on interaction data and interaction feedback data associated with the interaction (Olmstead: Para 0056-0057 via The system 100 configures a machine learning server 204 to implement natural language processing rules to determine whether electronic communications, such as emails, are written in a formal or informal manner, or in a positive or negative manner. The machine learning server 204 uses different relationship factors to generate or update scores for relationships between contacts. Example relationship factors include formality, tone, frequency, and timing of electronic communications. Real-time and historical electronic communications can be used. Each electronic communication between those contacts or individuals may be processed by machine learning server 204 to update the score. The machine learning server 204 can stored the relationship scores and data related thereto (including some or all electronic communications) in graph data storage 202. The graph data storage 202 can be a distributed graph storage servers, for example. Contact data storage 204 persistently stores data related to contacts, such as individuals and entities, for example, including metadata and attributes about contacts, such as company, title, phone, address, electronic address, and so on. Communication data storage persistently stores 206 data related to electronic communications. Query device 104 queries system 100 with search requests for connections to various contacts (e.g. individuals, entities). The system 100 configures a presentation server 206 to interact with machine learning server 204 to respond to search requests. Presentation server 206 queries the database of relationship scores in graph data storage 202 to determine what connection paths exist between contacts and the strength of those connections. Presentation server 206 generates visual representations of contacts, what connection paths exist between contacts and the strength of those connections using different visual effects. For example, nodes of a graph can visually represent contacts and edges between the nodes can represent a connection path between contacts. The edges can be assigned values or weights to represent different scores. The edges and scores are iteratively updated as new electronic communications are processed by machine learning server 204).
However, Olmstead does not explicitly disclose the limitations of Claim 1 which state selecting, based on the categorization of the interaction, an indication of a probability that the score associated with the interaction should be revised; wherein interaction data comprises data extracted from the interaction; interaction feedback data comprises data extracted from feedback about the interaction; and the score revision decision tree comprises a decision tree data structure comprising at least one decision node, each decision node corresponding to at least one data point of the interaction data or interaction feedback data.
Abitbol though, with the teachings of Olmstead, teaches of selecting, based on the categorization of the interaction, an indication of a probability that the score associated with the interaction should be revised (Abitbol: Para 0029, 0044 via Moreover, by statistically analyzing large amounts of historical data of the conversational computer program, probable paths of conversation towards desirable, undesirable, and neutral fulfillment nodes may be learned, automatically. The polarity scores of the dialogue nodes may then be updated based on the learned probable paths, under the premise that dialogue nodes that lead more often to a certain type of fulfillment node (e.g., desirable, undesirable, neutral) should be scored accordingly with this tendency...As more and more historical data of past conversations is gathered, the probable paths of conversation towards desirable, undesirable, and neutral fulfillment nodes may be learned, automatically. With reference to FIG. 4, the probability labels of the edges may change over time, and therefore also the weighed probabilities of entire paths that traverse multiple dialogue nodes towards a fulfillment node. The polarity scores of the dialogue nodes may then be updated based on the learned probable paths, under the premise that dialogue nodes that lead more often to a certain type of fulfillment node (e.g., desirable, undesirable, neutral) should be scored accordingly with this tendency);
wherein interaction data comprises data extracted from the interaction; interaction feedback data comprises data extracted from feedback about the interaction; and the score revision decision tree comprises a decision tree data structure comprising at least one decision node, each decision node corresponding to at least one data point of the interaction data or interaction feedback data (Abitbol: Para 0037, 0042 via The historical data obtained in 206 may refer to past executions of conversations by the conversational computer program. The historical data may include the exact nodes traversed during those conversations, and the order of traversal. For example, with reference to the conversational flow of FIG. 3, a historical record of a single conversation may be: {Conversation Start>Dialogue Node #2>Dialogue Node #8>Fulfillment Node #1}...FIG. 4 illustrates this. Its conversation path 400, similar to FIG. 3, starts at a block 402 and traverses dialogue nodes 404 through to fulfillment nodes 406. The edges between the various nodes are labeled with their probabilities. For example, as the conversation starts 402, there is equal probability (0.25) that each of dialogue nodes #1-4 will occur, and so on and so forth).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Olmstead with the teachings of Abitbol in order to have selecting, based on the categorization of the interaction, an indication of a probability that the score associated with the interaction should be revised; wherein interaction data comprises data extracted from the interaction; interaction feedback data comprises data extracted from feedback about the interaction; and the score revision decision tree comprises a decision tree data structure comprising at least one decision node, each decision node corresponding to at least one data point of the interaction data or interaction feedback data. The motivations behind this being to incorporate the teachings of conversational computing. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 3, the combination of Olmstead/Abitbol teaches the limitations of Claim 3 which state
revising the score associated with the transaction based on the indication of a probability that the score associated with the interaction should be revised, wherein revising the score comprises updating at least one value indicative of the score (Abitbol: Para 0029, 0044 via Moreover, by statistically analyzing large amounts of historical data of the conversational computer program, probable paths of conversation towards desirable, undesirable, and neutral fulfillment nodes may be learned, automatically. The polarity scores of the dialogue nodes may then be updated based on the learned probable paths, under the premise that dialogue nodes that lead more often to a certain type of fulfillment node (e.g., desirable, undesirable, neutral) should be scored accordingly with this tendency...As more and more historical data of past conversations is gathered, the probable paths of conversation towards desirable, undesirable, and neutral fulfillment nodes may be learned, automatically. With reference to FIG. 4, the probability labels of the edges may change over time, and therefore also the weighed probabilities of entire paths that traverse multiple dialogue nodes towards a fulfillment node. The polarity scores of the dialogue nodes may then be updated based on the learned probable paths, under the premise that dialogue nodes that lead more often to a certain type of fulfillment node (e.g., desirable, undesirable, neutral) should be scored accordingly with this tendency).
Regarding Claim 4, the combination of Olmstead/Abitbol teaches the limitations of Claim 4 which state
wherein the interaction feedback data comprises at least one of: an indication of feedback categories; and an indication of feedback sentiment (Olmstead: Para 0066 via Machine learning server 204 implements a feedback mechanism that can be direct or indirect to refine the NLP of electronic communications and score calculation. For example, query device 104 can indicate a recommendation for a contact was useful and a good match or connection using a response confirmation (e.g. direct feedback). As another example, machine learning server 204 can automatically notice an increase in communication between two contacts after a recommendation and assume that the recommendation for the contact was successful (e.g. derived feedback)).
Regarding Claim 5, the combination of Olmstead/Abitbol teaches the limitations of Claim 5 which state
wherein the interaction data comprises at least one of: an indication of interaction categories; an indication of interaction sentiment; and an indication of interaction frustration (Olmstead: Para 0013, 0081 via In accordance with another aspect, there is provided a process for electronic communications. The process involves intercepting an electronic communication in real-time between a recipient and a sender. The electronic communication can also refer to or mention other individuals or entities or contacts. For example, social media messages can refer to or mention another contact or entity using @entity or #entity or a plain text reference to the entity. The electronic communication can be used to update relationship scores for the sender, recipient and other individuals or entities referred to in the electronic communication. The process involves classifying the electronic communication using natural language processing to determine a sentiment classification and a formality classification. The process involves calculating a relationship score for the recipient and the sender based (or other entities referred to in the electronic communication) on the classified electronic communication, the sentiment classification and the formality classification. The process involves updating or creating in real-time an edge in a graph structure between nodes representing the recipient and the sender, the update based on the relationship score. The process involves receiving a query identifying a target contact. The process involves identifying a connection pathway within the graph structure to the target contact, the connection pathway including one or more edges and one or more nodes. The process involves generating visual effects for the connection pathway and graph structure for display on a computing device... The natural language processing engine 406, network graph composer 408, the machine learning sentimental categorizer 412, and the network graph optimal path solver 410 implement aspects of the machine learning server 204 according to some embodiments. The natural language processing engine 406 receives electronic communications (from communication data 402) in real-time and is configured with a natural language parser to process the electronic communications. Parsing or syntactic analysis is the process of analysing a string of symbols (e.g. natural language or computer language) conforming to the rules of a formal grammar. The natural language parser includes program instructions and the formal grammar to identify grammatical structure of sentences and groups of words that form phrases. The natural language parser determines words that are the subject or object of a verb. For example, probabilistic parsers use knowledge of language gained from parsed sentences to try to predict a likely analysis of new sentences. The machine learning sentimental categorizer 412 generates a score for the parsed communication languages by classifying the tone or sentiment and the formality, among other relationship factors. The network graph composer 408 generates or updates edges between nodes of a graph structure based on the generated scores. The network graph optimal path solver 410 identifies path connections between nodes to provide recommended contacts in response to queries from the presentation engine 414. The network graph optimal path solver 410 implements different graph traversal processes based on the costs of the edges connecting different nodes as the edges represent relationship scores).
Regarding Claim 6, the combination of Olmstead/Abitbol teaches the limitations of Claim 6 which state
comparing the interaction data with the interaction feedback data to find a correspondence between the interaction data and the interaction feedback data, wherein categorizing the interaction is further based on the correspondence (Olmstead: Para 0059, 0086 via The system 100 can implement a feedback mechanism to allow one or both parties to approve or modify the computed relationship score. The system 100 indicates a score on query device 104 or user device 102 and receives feedback response confirmations in response. The system 100 can train machine learning servers 204 based on the response confirmations... At 508, the machine learning server 204 compares computed relationship factors to different thresholds. For example, the machine learning server 204 classifies the electronic communication as being formal or informal and of good or bad sentiment using threshold values).
Regarding Claims 8 and 10-13, they are analogous to Claims 1 and 3-6 respectively and are rejected for the same reasons (Olmstead: Para 0095).
Claim(s) 2 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Olmstead et al. (US 2018/0082678 A1) in view of Abitbol et al. (US 2021/0004885 A1) further in view of Pirat et al. (US 2018/0084111 A1).
Regarding Claim 2, while the combination of Olmstead/Abitbol teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 2 which state normalizing the interaction data and the interaction feedback data.
Pirat though, with the teachings of Olmstead/Abitbol, teaches of
normalizing the interaction data and the interaction feedback data (Pirat: Para 0186 via he collected data is streamlined and baselined in act 1220. In this regard, the module 260 streamlines and baselines the data source by, for example, normalizing the units of the various data to a standard set of units (e.g., normalizing data to events per minute, where the original data may have been stored as events per day or minutes per event), normalizing the data to similar ranges (e.g., a value from 0 to 100 or a value from 0 to 255), accumulating or averaging values, and/or the like).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Olmstead/Abitbol, with the teachings of Pirat in order to have normalizing the interaction data and the interaction feedback data. The motivations behind this being to incorporate the teachings of managing a multimodal engagement. Furthermore, simple substitution of one known element for another to obtain predictable results and would be obvious to try.
Regarding Claim 9, it is analogous to Claim 2 and is rejected for the same reasons.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Olmstead et al. (US 2018/0082678 A1) in view of Abitbol et al. (US 2021/0004885 A1) further in view of Korada et al. (US 2017/0329881 A1).
Regarding Claim 7, while the combination of Olmstead/Abitbol teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 7 which state displaying to a user the indication of the probability that the score associated with the interaction should be revised; and revising the score based on a user input, wherein revising the score comprises updating at least one value indicative of the score stored in a memory.
Korada though, with the teachings of Olmstead/Abitbol, teaches of
displaying to a user the indication of the probability that the score associated with the interaction should be revised; and revising the score based on a user input, wherein revising the score comprises updating at least one value indicative of the score stored in a memory (Korada: Para 0056 via Further aspects of the Page 17 disclosed subject matter include methods. Once such method is disclosed in FIG. 10. A method 1000 for adaptive real time modeling and scoring may comprise: at 1002, determining, based on a threshold or trigger, for example a trigger such as a detection of new significant relationships in historic or recent data, whether a predictive scoring model is ready for a refresh or regeneration; at 1004, receiving and transforming user-selectable system input data, the user-selectable system input data comprising at least one of email, display or social media traffic; at 1006, (optional) identifying for the predictive scoring model a scoring model for assigning scores to leads; at 1008, monitoring and identifying a change in the input data and, based on an identified change in the input data, automatically refreshing or regenerating the scoring model for calculating new lead scores; and, at 1010, outputting a refreshed or regenerated scoring model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Olmstead/Abitbol, with the teachings of Korada in order to have displaying to a user the indication of the probability that the score associated with the interaction should be revised; and revising the score based on a user input, wherein revising the score comprises updating at least one value indicative of the score stored in a memory. The motivations behind this being to incorporate the teachings of accurate and efficient adaptive real time modeling and scoring. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 14, it is analogous to Claim 7 and is rejected for the same reasons.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TYRONE E SINGLETARY whose telephone number is (571)272-1684. The examiner can normally be reached 9 - 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, Rutao Wu can be reached at 571-272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/T.E.S./ Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623