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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/03/2026 has been entered.
Status of the Claims
Claims 1-2, 4-9 and 11-14 are pending in the instant patent application. Claims 1 and 8 are amended. Claims 3 and 10 are cancelled.
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 and are updated and addressed below in light of the amendments and 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 and are updated and addressed below in light of the amendments.
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 and Examiner further maintains their stance as noted in the previous Office Action.
Regarding Applicant’s arguments that currently amended claims do not recite abstract ideas, Examiner respectfully disagrees and maintains that the claims as presented recite abstract ideas. Applicant asserts that due to the use of a computer that the claim limitations cannot fall within the Mental Processes grouping of abstract ideas. However, Examiner will note that 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 (see MPEP 2106.04(a)(2)(III)(C)). Furthermore, the limitations are mere instructions to use the computer as a tool to carry out the abstract idea.
Regarding Applicant’s arguments that the amended claims directly address Examiner’s concern under Ex Parte Desjardins and any alleged improvements, Examiner respectfully disagrees and maintains their stance as stated in the previous Office Action. Examiner finds Applicant’s arguments are directed to improvements to an existing business process, automated by the present inventions additional elements without demonstrating an improvement to the technology, technological field, or computer-related technology.
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 1-2 and 4-7, they are directed to a method, however the claims are rejected because they are directed to a judicial exception without significantly more. Claims 1-2 and 4-7 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 in response to a request to revise the score associated with the computer-based interaction: 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 the indication of the probability is provided based on a leaf node reached upon the traversal of the score revision decision tree; and revising the score associated with the interaction based on the indication of the probability, wherein the revising of the score comprises replacing an old score by a new score, the revising of the score by changing a data value stored; 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). 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 (see MPEP 2106.04(a)(2)(III)(C)).
Accordingly, the claim recites an abstract idea and dependent claims 2 and 4-7 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 and computer memory. The computer processor and computer memory are merely 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. Claim 1 includes various elements that are not directed to the abstract idea under 2A. These elements include a computer processor, computer memory 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 8-9 and 11-14, they are directed to a system, however the claims are rejected because they are directed to a judicial exception without significantly more. Claims 8-9 and 11-14 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 8, claim 8 recites in response to a request to revise the score associated with the interaction 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 the indication of the probability is provided based on a leaf node reached upon the traversal of the score revision decision tree; and revise the score associated with the interaction based on the indication of the probability, wherein the revising of the score comprises replacing an old score by a new score, the revising of the score by changing a data value stored; 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). 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 (see MPEP 2106.04(a)(2)(III)(C)).
Accordingly, the claim recites an abstract idea and dependent claims 9 and 11-14 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 8-9 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 8-9 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. Furthermore, Applicant’s arguments are moot in light of newly amended language and newly cited art.
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, 4-6, 8 and 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jeppson et al. (US 2020/0185069 A1) in view of Olmstead et al. (US 2018/0082678 A1) in view of Abitbol et al. (US 2021/0004885 A1) further in view of Zhao et al. (US 2013/0339202 A1).
Regarding Claim 1, Olmstead teaches the limitations of Claim 1 which state
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);
wherein interaction data comprises data extracted from the interaction (Olmstead: Para 0013, 0081 via 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 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);
interaction feedback data comprises data extracted from feedback about the interaction (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));
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 (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 limitation of Claim 1 which states selecting, based on the categorization of the interaction, an indication of a probability that the score associated with the interaction should be revised.
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).
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. The motivations behind this being to incorporate the teachings of conversational computing and automating the evaluation of human-computer conversations for the calculating of polarity scores and desirability scores as taught by Abitbol. 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.
Furthermore, Olmstead does not explicitly disclose the limitation of Claim 1 which states wherein the indication of the probability is provided based on a leaf node reached upon the traversal of the score revision decision tree.
Zhao though, with the teachings of Olmstead/Abitbol, teaches of
wherein the indication of the probability is provided based on a leaf node reached upon the traversal of the score revision decision tree (Zhao: Para 0038-0039 via Decision tree (DT) models 144 can capture the nonlinearity between data and their labels. Unlike the LR model 142, the DT model 144 can be constructed to take into account multiple hospitals (e.g., 32,000 decision tree models can be constructed). Here, the probability p(x.sub.i|x.sub.-i) is modeled as a decision tree, which consists of decision nodes and leaf nodes. Each of the decision nodes consists of the feature used to split the node, and links to other nodes based on presence or absence of the feature in a given test case. Each leaf node consists of probability of the presence of code. The decision tree is constructed by minimizing entropy, which is defined as -.SIGMA..sub.xp(x)log p (x). At the root node, the feature that minimizes entropy of the label is selected. The samples are then split into two groups based on the value of the split feature and recursively subsequent nodes are created. The process stops when there are insufficient samples to proceed or the entropy reduction is not substantial. At every leaf node, the probability of the label is calculated as (number of positive labels)/(number of labels), and stored. During scoring, the decision tree is traversed according to the values of the decision features, and when a leaf node is reached, the label probability associated with that leaf node is returned).
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 Zhao in order to have wherein the indication of the probability is provided based on a leaf node reached upon the traversal of the score revision decision tree. The motivations behind this being to incorporate the teachings of utilizing a decision tree to model probabilities. 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.
In addition, Olmstead does not explicitly disclose the limitations of Claim 1 which states in response to a request to revise the score associated with the computer-based interaction; revising the score associated with the interaction based on the indication of the probability, wherein the revising of the score comprises replacing an old score by a new score, the revising of the score by changing a data value stored in computer memory.
Jeppson though, with the teachings of Olmstead/Abitbol/Zhao, teaches of
in response to a request to revise the score associated with the computer-based interaction (Jeppson: Para 0069 via system 20 accommodates rebuttal requests but limits the requests to individuals who fell outside the margin of error on their audit. In one such example approach, system 20 sets up a rebuttal session between the auditor (at user interface 28) and the coding professional (at user interface 18) in which the coding professional can attempt to rebut the marking as incorrect of one or more of the codings. The interaction being the rebuttal session between an auditor and the coding professional);
revising the score associated with the interaction based on the indication of the probability, wherein the revising of the score comprises replacing an old score by a new score, the revising of the score by changing a data value stored in computer memory (Jeppson: Para 0069 via system 20 accommodates rebuttal requests but limits the requests to individuals who fell outside the margin of error on their audit. In one such example approach, system 20 sets up a rebuttal session between the auditor (at user interface 28) and the coding professional (at user interface 18) in which the coding professional can attempt to rebut the marking as incorrect of one or more of the codings. (124) If the rebuttal session results in changes to the coding professional's accuracy score, system 20 updates its records).
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/Zhao with the teachings of Jeppson in order to have in response to a request to revise the score associated with the computer-based interaction; revising the score associated with the interaction based on the indication of the probability, wherein the revising of the score comprises replacing an old score by a new score, the revising of the score by changing a data value stored in computer memory. The motivations behind this being to incorporate the teachings of utilizing a margin of error when it pertains to submitting rebuttals on interactions involving an audit score between an auditor and coding professional. Furthermore, simple substitution of one known element for another would yield predictable results.
Regarding Claim 4, the combination of Olmstead/Abitbol/Zhao/Jeppson teaches the limitations of Claim 4 which states
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/Zhao/Jeppson teaches the limitations of Claim 5 which states
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/Zhao/Jeppson teaches the limitations of Claim 6 which states
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 11-13, they are analogous to Claims 1 and 4-6 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 Jeppson et al. (US 2020/0185069 A1) in view of Olmstead et al. (US 2018/0082678 A1) in view of Abitbol et al. (US 2021/0004885 A1) in view of Zhao et al. (US 2013/0339202 A1) further in view of Pirat et al. (US 2018/0084111 A1).
Regarding Claim 2, while the combination of Olmstead/Abitbol/Zhao/Jeppson 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/Zhao/Jeppson, 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/Zhao/Jeppson, 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 Jeppson et al. (US 2020/0185069 A1) in view of Olmstead et al. (US 2018/0082678 A1) in view of Abitbol et al. (US 2021/0004885 A1) in view of Zhao et al. (US 2013/0339202 A1) further in view of Laks (US 2016/0350675 A1).
Regarding Claim 7, while the combination of Olmstead/Abitbol/Zhao/Jeppson teaches the limitations of Claim 1, and the limitation of Claim 7 which states
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 (Jeppson: Para 0069 via If the rebuttal session results in changes to the coding professional's accuracy score, system 20 updates its records).
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.
Laks though, with the teachings of Olmstead/Abitbol/Zhao/Jeppson, teaches of
displaying to a user the indication of the probability that the score associated with the interaction should be revised (Laks: Para 0026, 0031-0032, 0058-0059 via determining a score associated with a flagged item indicating a probability that the item is objectionable and sending it for human review. The machine learning probability score is displayed alongside to the item in a user interface to inform a manual review).
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/Zhao/Jeppson, with the teachings of Laks in order to have displaying to a user the indication of the probability that the score associated with the interaction should be revised. The motivations behind this being to incorporate the teachings of a probability-informed manual review interface as taught by Laks. 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Idan et al. (US 2008/0152122 A1)
Reisman (US 2011/0295722 A1)
Lai et al. (US 2017/0286962 A1)
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, Beth Boswell can be reached at 571-272-6737. 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 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
Prosecution Insights