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 .
This Office Action has been issued in response to Applicant’s Communication of application S/N 18/443,614 filed on February 16, 2024. Claims 1-20 are pending with the application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With respect to claim 1, the limitations directed towards determine patentable concepts, identify, one or more patentable concepts in the one or more email messages; compare the one or more patentable concepts to a patent database including published patent applications with respective publication dates on or before the date of database access, wherein comparing includes generating a comparison document indicating at least one difference and/or at least one similarity between the one or more patentable concepts and the patent applications of the patent database, wherein the comparing includes using the ML chatbot; and generate, based upon the comparison, one or more novelty scores for each of the one or more patentable concepts, is a process that, under its broadest reasonably interpretation, covers performance of these limitations in the mind but for the recitation of generic computer components. That is, other than reciting a computer system for analyzing emails to determine patentable concepts, the computer system comprising one or more processors configured to: access one or more email messages and a machine learning (ML) chatbot trained to analyze text and generate text, nothing in the claim precludes these steps from practically being performed in the mind and/or by a human with pen and paper.
For example, but for the limitations stating a computer system for analyzing emails to determine patentable concepts, the computer system comprising one or more processors configured to: access one or more email messages and a machine learning (ML) chatbot trained to analyze text and generate text, the mention of determine patentable concepts, identify, one or more patentable concepts in the one or more email messages; compare the one or more patentable concepts to a patent database including published patent applications with respective publication dates on or before the date of database access, wherein comparing includes generating a comparison document indicating at least one difference and/or at least one similarity between the one or more patentable concepts and the patent applications of the patent database, wherein the comparing includes using the ML chatbot; and generate, based upon the comparison, one or more novelty scores for each of the one or more patentable concepts, in the context of this claim, encompasses mentally determining patentable concepts and novelty by conducting a comparison analysis. If a claim limitation, under its broadest reasonable interpretation, covers performance of these limitations in the mind but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application by additional elements. In particular, a computer system for analyzing emails to determine patentable concepts, the computer system comprising one or more processors configured to: access one or more email messages and a machine learning (ML) chatbot trained to analyze text and generate text. The recitation of a computer system for analyzing emails to determine patentable concepts, the computer system comprising one or more processors configured to: access one or more email messages and a machine learning (ML) chatbot trained to analyze text and generate text is recited at a high level of generality (i.e., as a generic computer performing a generic computer function of search) such that it amounts to no more than mere instructions to apply the exception. A computer system for analyzing emails to determine patentable concepts, the computer system comprising one or more processors configured to: access one or more email messages and a machine learning (ML) chatbot trained to analyze text and generate text is interpreted by the examiner to be insignificant extra solution activity and it merely confines the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. These elements do not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea.
This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, a computer system for analyzing emails to determine patentable concepts, the computer system comprising one or more processors configured to: access one or more email messages and a machine learning (ML) chatbot trained to analyze text and generate text is recited at a high level of generality to apply the exception using generic components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The recitation of a computer system for analyzing emails to determine patentable concepts, the computer system comprising one or more processors configured to: access one or more email messages and a machine learning (ML) chatbot trained to analyze text and generate text is interpreted to be well understood, routine and conventional activity (Receiving or transmitting data over a network, Symantec (see MPEP 2106.06(d))). To further elaborate, the additional limitations of a computer system for analyzing emails to determine patentable concepts, the computer system comprising one or more processors configured to: access one or more email messages and a machine learning (ML) chatbot trained to analyze text and generate text does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use. Claim 1 is not patent eligible.
Claim 18 recites similar limitations as in claim 1. Therefore claim 18 ise rejected for the same reasons as set forth above. See claim 1 for analysis.
With respects to claims 2 and 19, the limitations are directed towards training, via the one or more processors, the ML chatbot based upon historical data comprising: (i) independent variables comprising historical email messages, and/or (ii) dependent variables comprising historical patentable concepts. These additional elements do not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use. Therefore, claims 2 and 19 do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claims 3 and 20, the limitations are directed towards the one or more processors are further configured to identify, via the ML chatbot, one or more patentable concepts associated with a predefined topic or a predefined technical area. These additional elements do not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use. Therefore, claims 3 and 20 do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claim 4, the limitations are directed towards access the one or more email messages at predetermined time intervals; and alert a user with identified one or more patentable concepts when one or more novelty scores exceed a predefined threshold. These additional elements do not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use. Therefore, claim 4 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claim 5, the limitations are directed towards interview, via the ML chatbot or a voice bot, a user; and analyze, via the ML chatbot or a voice bot, an interview transcript to determine one or more patentable concepts from the interview. . These additional elements are interpreted to be well understood, routine and conventional activity (Receiving or transmitting data over a network, Symantec (see MPEP 2106.06(d))). Therefore, claim 5 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claim 6, the limitations are directed towards the patent database includes one or more public access patent databases and/or one or more internal intellectual property databases. These additional elements do not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use. Therefore, claim 6 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claims 7, the limitations are directed towards wherein the one or more processors are further configured to, via the ML chatbot: identify, from the accessed one or more email messages, one or more potential questions associated with a scheduled meeting; identify percentage of the scheduled meeting attendees associated with the each one or more potential questions; determine probability of the each one or more potential questions being asked; generate suggested answers to the each one or more potential questions; and send the list of identified one or more potential questions and their suggested answers to a meeting organizer. The recitation of identify, from the accessed one or more email messages, one or more potential questions associated with a scheduled meeting; identify percentage of the scheduled meeting attendees associated with the each one or more potential questions, determine probability of the each one or more potential questions being asked, generate suggested answers to the each one or more potential question further elaborates the abstract idea and the human mind and/or with pen and paper can identify, from the accessed one or more email messages, one or more potential questions associated with a scheduled meeting; identify percentage of the scheduled meeting attendees associated with the each one or more potential questions, determine probability of the each one or more potential questions being asked, generate suggested answers to the each one or more potential question. The recitation of send the list of identified one or more potential questions and their suggested answers to a meeting organizer is interpreted to be well understood, routine and conventional activity (Receiving or transmitting data over a network, Symantec (see MPEP 2106.06(d))). Therefore, claim 7 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claim 9, the limitations are directed towards determine, from the accessed one or more email messages, if there are discrepancies in viewpoints on a particular topic; and generate a report summarizing the discrepancies in viewpoints. These elements further elaborates the abstract idea and the human mind and/or with pen and paper can determine, from the accessed one or more email messages, if there are discrepancies in viewpoints on a particular topic; and generate a report summarizing the discrepancies in viewpoints. Therefore, claim 9 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claim 10, the limitations are directed towards receive, at the ML chatbot, a list of employees associated with one or more projects; analyze, via the ML chatbot, the accessed one or more email messages, and label email messages associated with the one or more projects; identify, via the ML chatbot, email message authors of the labeled email messages; identify, via the ML chatbot, select labeled email messages, associated with projects which authors are not associated with the one or more projects; and generate, via the ML chatbot, an email message to the select labeled email messages authors, inviting the select labeled email messages authors to connect with the employees associated with the one or more projects. These additional elements are interpreted to be well understood, routine and conventional activity (Receiving or transmitting data over a network, Symantec (see MPEP 2106.06(d))). Therefore, claim 10 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claim 11, the limitations are directed towards training, via the one or more processors, the ML chatbot based upon historical data comprising: (i) independent variables comprising historical email messages, and/or (ii) independent variables comprising descriptions of different projects, and/or (iii) dependent variables comprising associations between historical email messages and descriptions of different projects. These additional elements do not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use. Therefore, claim 11 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claim 12, the limitations are directed towards analyze, via the ML chatbot, the accessed one or more email messages to determine an employee satisfaction score. These elements further elaborates the abstract idea and the human mind and/or with pen and paper can analyze, via the ML chatbot, the accessed one or more email messages to determine an employee satisfaction score. Therefore, claim 12 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claim 13, the limitations are directed towards training, via the one or more processors, the ML chatbot based upon historical data comprising: (i) independent variables comprising historical texts, and/or (ii) dependent variables comprising satisfaction rating associated with the authors and/or topics of the respective historical texts. These additional elements do not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use. Therefore, claim 13 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claim 14, the limitations are directed towards the satisfaction score is associated with at least a particular project, a particular team, or with a workplace. These elements further elaborates the abstract idea and the human mind and/or with pen and paper can associate the satisfaction score can with at least a particular project, a particular team, or with a workplace. Therefore, claim 14 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claim 15, the limitations are directed towards the one or more email messages includes video data, and the ML chatbot extracts and analyzes employee facial expression from the video data. These additional elements do not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use. Therefore, claim 15 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claim 16, the limitations are directed towards analyze, via the ML chatbot, contents of a received email message from the accessed one or more email messages; determine, via the ML chatbot, a tone of the analyzed received email message; and generate, via the ML chatbot, one or more responses to the analyzed received email message, at least one of the responses using the determined tone of the analyzed received email message. These elements further elaborates the abstract idea and the human mind and/or with pen and paper can analyze, via the ML chatbot, contents of a received email message from the accessed one or more email messages, determine, via the ML chatbot, a tone of the analyzed received email message, and generate, via the ML chatbot, one or more responses to the analyzed received email message, at least one of the responses using the determined tone of the analyzed received email message. Therefore, claim 16 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claim 17, the limitations are directed towards training, via the one or more processors, the ML chatbot based upon historical data comprising: (i) independent variables comprising historical texts, and/or (ii) dependent variables comprising tone of the historical texts. These additional elements do not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use. Therefore, claim 17 does not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
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 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.
Claim(s) 1, 2, 3, 4, 6, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lenchner et al (U.S. Publication No.: US 20190114395 A1) hereinafter Lenchner, Greaser et al. (U.S. Publication No.: US 20220114340 A1) hereinafter Greaser, and further in view of Lepeltier (U.S. No.: US 20170075877 A1) hereinafter Lepeltier.
As to claim 1:
Lenchner discloses:
A computer system for analyzing emails to determine patentable concepts, the computer system comprising one or more processors configured to:
access one or more email messages [Paragraph 0064 teaches the data sources 401-405 may be analyzed by an NLP system 410 (and a transcription component 439 if necessary) to data mine or transcribe relevant information from the content of the data sources 401-405 (e.g., intellectual property content contributions such as methods and features extracted from mined emails, reports, notes, scientific papers or documents). Paragraph 0065 teaches The NLP system 410 may consume the multiple data sources 401-405 as selected by using a data source input component 408, including, for example, word docs, emails.];
identify, via a machine learning (ML) chatbot trained to analyze text and generate text, one or more patentable concepts in the one or more email messages [Paragraph 0073 teaches the intellectual property identification system 430 may include a user interface (“UP”) component 434 (e.g., an interactive graphical user interface “GUI”) providing user interaction with the indexed content for mining and navigation and/or receiving one or more inputs/queries from a user such as, for example, a request from a user “Who are the inventors that need to sign documents for patent application “X”?”. Paragraph 0080 teaches the intellectual property identification system 430 may also include a machine learning component 438. The machine learning component 438 may apply one or more heuristics and machine learning based models];
a machine learning (ML) chatbot [Paragraph 0073 teaches the intellectual property identification system 430 may include a user interface (“UP”) component 434 (e.g., an interactive graphical user interface “GUI”) providing user interaction with the indexed content for mining and navigation and/or receiving one or more inputs/queries from a user such as, for example, a request from a user “Who are the inventors that need to sign documents for patent application “X”?”.]
Lenchner discloses some of the limitations as set forth in claim 1 but does not appear to expressly disclose compare the one or more patentable concepts to a patent database including published patent applications, wherein comparing includes generating a comparison document indicating at least one difference and/or at least one similarity between the one or more patentable concepts and the patent applications of the patent database, wherein the comparing includes using the ML, and generate, based upon the comparison, one or more novelty scores for each of the one or more patentable concepts, patent database including published patent applications with respective publication dates on or before the date of database access.
Greaser discloses:
compare the one or more patentable concepts to a patent database including published patent applications [Paragraph 0074 teaches the user idea is compared to the prior art references in a defined vector space, through the above system. Transformation to vector space is achieved by embedding the prior art reference text information and the idea. Paragraph 0127 teaches at 1206, each chunk is preferably compared to a user input idea and/or other document chunk… The comparison between each chunk and the user input idea, other document, or chunk thereof, may be performed for example according to the well-known and widely used cosine similarity metric.], wherein comparing includes generating a comparison document indicating at least one difference and/or at least one similarity between the one or more patentable concepts and the patent applications of the patent database [Paragraph 0112 teaches by “document”, it is meant any text featuring a plurality of words. Paragraph 0119 teaches the tokens may correspond directly to data components, for use in preparing the output report as described in greater detail below.], wherein the comparing includes using the ML [Paragraph 0127 teaches At 1206, each chunk is preferably compared to a user input idea and/or other document chunk… The comparison between each chunk and the user input idea, other document, or chunk thereof, may be performed for example according to the well-known and widely used cosine similarity metric. Paragraph 0128 teaches at 1208, a linear assignment algorithm is preferably performed on the matrix of pairwise comparisons from 1206, that were performed between the embedding of each chunk from 1204 and the embedding of each comparison document and for which the results of a metric comparison algorithm are known… Other suitable algorithms may include but are not limited to an auction algorithm, training a neural network to predict the assignment, an ML (machine learning) attention mechanism, or linear programming. Note: Generating comparisons and inputting comparisons into a linear assignment algorithm that outputs the comparison, wherein the linear assignment algorithm machine learning attention mechanism or neural network.]; and generate, based upon the comparison, one or more novelty scores for each of the one or more patentable concepts [Paragraph 0129 teaches the scores for the outcome of the linear assignment algorithm may be assigned such that these scores may be used to determine the similarity of the chunks (that is, each chunk from 1204 and each comparison document) at 1210. Paragraph 0131 teaches the scores are distributed over a probability interval (0, 100). Paragraph 0132 teaches the above described process may be used for locating one or more patents and/or patent applications for the purposes of determining patentability. Note: Scores that are generated indicating patentability based on 102 rejection analysis (see Paragraph 0098), wherein the 102 rejection analysis is interpreted to be a novelty analysis tied to the scoring reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Lenchner, by incorporating generating comparisons and inputting comparisons into a linear assignment algorithm that outputs the comparison, wherein the linear assignment algorithm machine learning attention mechanism or neural network, as taught by Greaser (see Paragraph 0074, 0098, 0112, 0127-0129, 0131, 0132), because both applications are directed to data analysis; incorporating generating comparisons and inputting comparisons into a linear assignment algorithm that outputs the comparison, wherein the linear assignment algorithm machine learning attention mechanism or neural network improves the performance of the system (see Greaser Paragraph 0090).
Lenchner and Greaser discloses some of the limitations as set forth in claim 1 but does not appear to expressly disclose patent database including published patent applications with respective publication dates on or before the date of database access.
Lepeltier discloses:
patent database including published patent applications with respective publication dates on or before the date of database access [Paragraph 0199 teaches the method comprising detecting the first occurrence of said word or phrase in a corpus corresponding to one or more patent classification entries, storing associated dates (e.g. priority date, publication date).]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Lenchner and Greaser, by incorporating storing publication and priority dates associated patent documents, as taught by Lepeltier (see Paragraph 0199), because the three applications are directed to data analysis; incorporating storing publication and priority dates associated patent documents facilitates or improves or allows visibility and/or searchability and/or readability (see Lepeltier Paragraph 0054).
Claim 18 recites similar limitations as in claim 1. Therefore claim 18 is rejected for the same reasons as set forth above. See claim 1 for analysis.
As to claim 2:
Lenchner discloses:
The computer system of claim 1, further comprising training, via the one or more processors, the ML chatbot based upon historical data comprising: (i) independent variables comprising historical email messages [Paragraph 0060 teaches multiple data sources 401-405 may be provided by one or more content contributors (e.g., inventors, engineers, scientists, or persons developing intellectual property “ideas”). The data sources 401-405 may be provided as a corpus or group of data sources defined and/or identified. The data sources 401-405 may include, but are not limited to, data sources relating to one or more documents, materials related to email. Paragraph 0066 teaches the NLP system 410 may include a content consuming component 411 for inputting the data sources 401-405 and running its NLP and AI tools against them, learning the content, such as by using the machine learning component 438.], and/or (ii) dependent variables comprising historical patentable concepts.
Claim 19 recites similar limitations as in claim 2. Therefore claim 19 is rejected for the same reasons as set forth above. See claim 2 for analysis.
As to claim 3:
Lenchner discloses:
The computer system of claim 2, wherein the one or more processors are further configured to identify, via the ML chatbot, one or more patentable concepts associated with a predefined topic or a predefined technical area [Paragraph 0073 teaches the intellectual property identification system 430 may include a user interface (“UP”) component 434 (e.g., an interactive graphical user interface “GUI”) providing user interaction with the indexed content for mining and navigation and/or receiving one or more inputs/queries from a user such as, for example, a request from a user “Who are the inventors that need to sign documents for patent application “X”?”. Paragraph 0080 teaches the intellectual property identification system 430 may also include a machine learning component 438. The machine learning component 438 may apply one or more heuristics and machine learning based models].
Claim 20 recites similar limitations as in claim 3. Therefore claim 20 is rejected for the same reasons as set forth above. See claim 3 for analysis.
As to claim 4:
Lenchner discloses:
The computer system of claim 2, wherein the one or more processors are further configured to: access the one or more email messages at predetermined time intervals [Paragraph 0071 teaches the immutable ledger 420 may track, identify, and associate all communication threads of all intellectual property data generated during all stages of the development or “life cycle” of the intellectual property data. Paragraph 0076 teaches the intellectual property identification system 430 may also include a tracking component 435 for monitoring, tracking, tracing, and/or identifying content contributions, connections, or relationships between the intellectual property data and a content contributor ]; and alert a user with identified one or more patentable concepts when one or more novelty scores exceed a predefined threshold [Paragraph 0057 teaches workloads layer 90 provides examples of functionality for which the cloud computing environment may be utilized…various intellectual property data ownership establishment workloads and functions 96. In addition, workloads and functions 96 may include such operations as data analytics, data analysis, and as will be further described, notification functionality. Paragraph 0077 teaches the analyzing component 437 may assign a score, ranking, or credit such as, for example, a contribution score to the content contributed for each content contributor to intellectual property data. Paragraph 0079 teaches the calculation or computation operations may be performed using various mathematical operations or functions… by finding minimums, maximums or similar thresholds for combined variables, etc.).]
As to claim 6:
Lenchner, Greaser, and Lepeltier discloses all of the limitations as set forth in claim 1 and 2,
Greaser also discloses:
The computer system of claim 2, wherein the patent database includes one or more public access patent databases and/or one or more internal intellectual property databases [Paragraph 0050 as shown in FIG. 4B, a method 450 begins by obtaining full text patent application and/or patent data, for example from the USPTO.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Lenchner, by incorporating obtaining full text patent application and/or patent data, for example from the USPTO, as taught by Greaser (see Paragraph 0050), because both applications are directed to data analysis; incorporating obtaining full text patent application and/or patent data, for example from the USPTO improves the performance of the system (see Greaser Paragraph 0090).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lenchner et al (U.S. Publication No.: US 20190114395 A1) hereinafter Lenchner, Greaser et al. (U.S. Publication No.: US 20220114340 A1) hereinafter Greaser, in view of Lepeltier (U.S. No.: US 20170075877 A1) hereinafter Lepeltier, and further in view of Smith et al. (U.S. No.: US 20080266382 A1) hereinafter Smith.
As to claim 5:
Lenchner, Greaser, and Lepeltier discloses all of the limitations as set forth in claim 1 and 2 but does not appear to expressly disclose wherein the one or more processors are further configured to: interview, via the ML chatbot or a voice bot, a user; and 28 analyze, via the ML chatbot or a voice bot, an interview transcript to determine one or more patentable concepts from the interview.
Smith discloses:
The computer system of claim 2, wherein the one or more processors are further configured to: interview, via the ML chatbot or a voice bot, a user; and 28 analyze, via the ML chatbot or a voice bot, an interview transcript to determine one or more patentable concepts from the interview [Paragraph 0021 teaches the generated transcript of the meeting is linked with the video recording of the meeting via one or more "video hyperlinks". Accordingly, the transcript of the meeting can be subsequently searched based on a phrase and linked to the specific portion of the video containing the phrase. For example, if a user wants to review the contract terms that were discussed in recorded meeting, the generated transcript can be searched for the text phrase "Intellectual Property".]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Lenchner, Greaser, and Lepeltier, by incorporating generated transcript of the meeting is linked with the video recording of the meeting via one or more "video hyperlinks" and the generated transcript can be searched for the text phrase "Intellectual Property", as taught by Smith (see Paragraph 0021), because the four applications are directed to data analysis incorporating generated transcript of the meeting is linked with the video recording of the meeting via one or more "video hyperlinks" and the generated transcript can be searched for the text phrase "Intellectual Property" provides a searchable data stream is generated whereby specific portions of the data stream can be readily accessed (see Smith Paragraph 0007).
Claim(s) 7, 8, and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lenchner et al (U.S. Publication No.: US 20190114395 A1) hereinafter Lenchner, Greaser et al. (U.S. Publication No.: US 20220114340 A1) hereinafter Greaser, in view of Lepeltier (U.S. Publication No.: US 20170075877 A1) hereinafter Lepeltier, in view of Udupa et al. (U.S. Publication No.: US 20130151533 A1) hereinafter Udupa, and further in view of Rowland et al. (U.S. Patent No.: US 9270711 A1) hereinafter Rowland.
As to claim 7:
Lenchner, Greaser, and Lepeltier discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein the one or more processors are further configured to, via the ML chatbot: identify, from the accessed one or more email messages, one or more potential questions associated with a scheduled meeting determine probability of the each one or more potential questions being asked, generate suggested answers to the each one or more potential questions, and send the list of identified one or more potential questions and their suggested answers to a meeting organizer, identify percentage of the scheduled meeting attendees associated with the each one or more potential questions.
Udupa discloses:
The computer system of claim 1, wherein the one or more processors are further configured to, via the ML chatbot: identify, from the accessed one or more email messages, one or more potential questions associated with a scheduled meeting [Paragraph 0051 teaches the user may hover a cursor 610 over one of the suggested queries in the list of suggested queries 608, and responsive to detecting that the cursor 610 is hovered over a suggested query, a list of emails 612 that includes a threshold number of most recent emails that are retrievable when using the suggested query as a query over the emails is presented]; determine probability of the each one or more potential questions being asked [Paragraph 0047 teaches size of a query suggestion in a tag cloud may be indicative of probabilistic relevance to the user of the query suggestion, a number of emails will be returned responsive to executing a search using the query suggestion, or other information.]; generate suggested answers to the each one or more potential questions [Paragraph 0051 teaches the user may hover a cursor 610 over one of the suggested queries in the list of suggested queries 608, and responsive to detecting that the cursor 610 is hovered over a suggested query, a list of emails 612 that includes a threshold number of most recent emails that are retrievable when using the suggested query as a query over the emails is presented]; and send the list of identified one or more potential questions and their suggested answers to a meeting organizer [Paragraph 0051 teaches the user may hover a cursor 610 over one of the suggested queries in the list of suggested queries 608, and responsive to detecting that the cursor 610 is hovered over a suggested query, a list of emails 612 that includes a threshold number of most recent emails that are retrievable when using the suggested query as a query over the emails is presented].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Lenchner, Greaser, and Lepeltier, by incorporating a list of emails 612 that includes a threshold number of most recent emails that are retrievable when using the suggested query as a query over the emails, as taught by Udupa (see Paragraph 0047 and 0051), because the four applications are directed to data analysis; incorporating a list of emails 612 that includes a threshold number of most recent emails that are retrievable when using the suggested query as a query over the emails maximizes the benefit to the user (see Udupa Paragraph 0011).
Lenchner, Greaser, Lepeltier, and Udupa discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein the one or more processors are further configured to, via the ML chatbot: identify, from the accessed one or more email messages, one or more potential questions associated with a scheduled meeting determine probability of the each one or more potential questions being asked, generate suggested answers to the each one or more potential questions, and send the list of identified one or more potential questions and their suggested answers to a meeting organizer, identify percentage of the scheduled meeting attendees associated with the each one or more potential questions.
Rowland discloses:
identify percentage of the scheduled meeting attendees associated with the each one or more potential questions and meeting organizer [Column 16 Lines 55-57 teach not all attendees may answer the questions, in which case, the percentages provided are of percentages of those who answered the respective questions.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Lenchner, Greaser, Lepeltier, and Udupa, by incorporating percentages of those who answered the respective questions, as taught by Rowland (see Column 16 Lines 55-57), because the five applications are directed to data analysis; incorporating percentages of those who answered the respective questions provides an easy to use application That is, by combining an electronic scheduling engine, a meeting engine and a feedback engine together, the user does not have to open another application, send a text message, send an email, etc. in order to rate a meeting. Thus, a user frequently checking their calendar, or keeping their calendar active, may readily avail themselves of the advantageous features (see Rowland Column 11 Lines 11-17).
As to claim 8:
Lenchner, Greaser, Lepeltier, Udupa, and Rowloand discloses all of the limitations as set forth in claim 1 and 7.
Lenchner also discloses:
The computer system of claim 7, further comprising training, via the one or more processors, the ML chatbot based upon historical data comprising: (i) independent variables comprising historical questions [Paragraph 0060 teaches the data sources 401-405 may include, but are not limited to, data sources relating to one or more documents, materials related to emails. Paragraph 0069 teaches the immutable ledger 420 may also work in conjunction with an immutable ledger component 436 to maintain a timestamped record of all interactions and contributions of each content contributor to an intellectual property subject, topic, or idea.], and/or (ii) dependent variables comprising historical answers.
As to claim 9:
Lenchner, Greaser, Lepeltier, Udupa, and Rowloand discloses all of the limitations as set forth in claim 1 and 7.
Lenchner also discloses:
The computer system of claim 7, wherein the one or more processors are further configured to, via the ML chatbot, determine, from the accessed one or more email messages, if there are discrepancies in viewpoints on a particular topic; and generate a report summarizing the discrepancies in viewpoints [Paragraph 0083 teaches using the blockchain enables all persons to be aware of, and electronically attest (at so-called cryptographic strength) to, the contribution to the intellectual property so that conflicts cannot later arise and are eliminated. Paragraph 0088 teaches any content contributor who has created an idea may be tracked and identified as a content contributor (e.g., an “inventor”) to a particular intellectual property (e.g., an invention). Furthermore, one or more immutable ledgers enable identifying each person who participated in emails… the one or more immutable ledgers enable the identification of both persons that did and persons that did not contribute to the generation of ideas, suggestions, or concepts. The identified, non-contributor may be automatically identified as a non-inventor or not an author of all or portions of the intellectual property. The immutable legers ensure that participants are aware of inventorship and authorship to each idea, suggestion, recommendation, or even input throughout the content creation process (e.g., a starting state culminating in a final state of intellectual property).]
Claim(s) 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lenchner et al (U.S. Publication No.: US 20190114395 A1) hereinafter Lenchner, Greaser et al. (U.S. Publication No.: US 20220114340 A1) hereinafter Greaser, in view of Lepeltier (U.S. Publication No.: US 20170075877 A1) hereinafter Lepeltier, and further in view of Vendrow (U.S. Publication No.: US 20230092334 A1) hereinafter Vendrow.
As to claim 10:
Lenchner discloses:
The computer system of claim 1, wherein the one or more processors are further configured to: receive, at the ML chatbot, a list of employees associated with one or more projects; analyze, via the ML chatbot, the accessed one or more email messages, and label email messages associated with the one or more projects; identify, via the ML chatbot, email message authors of the labeled email messages; 29 identify, via the ML chatbot, select labeled email messages, associated with projects which authors are not associated with the one or more projects [Paragraph 0083 teaches using the blockchain enables all persons to be aware of, and electronically attest (at so-called cryptographic strength) to, the contribution to the intellectual property so that conflicts cannot later arise and are eliminated. Paragraph 0088 teaches any content contributor who has created an idea may be tracked and identified as a content contributor (e.g., an “inventor”) to a particular intellectual property (e.g., an invention). Furthermore, one or more immutable ledgers enable identifying each person who participated in emails… the one or more immutable ledgers enable the identification of both persons that did and persons that did not contribute to the generation of ideas, suggestions, or concepts. The identified, non-contributor may be automatically identified as a non-inventor or not an author of all or portions of the intellectual property. The immutable legers ensure that participants are aware of inventorship and authorship to each idea, suggestion, recommendation, or even input throughout the content creation process (e.g., a starting state culminating in a final state of intellectual property). Note: Identifying individuals who are and who are not inventors or authors of intellectual property (projects) based on emails historical email analysis reads on the claims.]; and
Lenchner, Greaser, and Lepeltier discloses all of the limitations as set forth in claim 1 and some of 10 but does not appear to expressly disclose generate, via the ML chatbot, an email message to the select labeled email messages authors, inviting the select labeled email messages authors to connect with the employees associated with the one or more projects.
Vendrow discloses:
generate, via the ML chatbot, an email message to the select labeled email messages authors, inviting the select labeled email messages authors to connect with the employees associated with the one or more projects [Paragraph 0140 teaches the processor may utilize representation learning and a neural network-style machine learning methods. Paragraph 0179 teaches at step 903, the processor generates an invite email. For example, an invite email may comprise an email addressed to the contact that includes a link to register for (that is, create an account with) the collaboration environment in the subject and/or body of the email. An example of an email having a link to register is depicted in GUI 2400 of FIG. 24.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Lenchner, Greaser, and Lepeltier, by incorporating generating an email invitation to collaborate, as taught by Vendrow (see Paragraph 0140 and 0179), because the four applications are directed to data analysis; incorporating generating an email invitation to collaborate is advantageously in that the processor may utilize representation learning and a neural network-style machine learning methods (see Vendrow Paragraph 0140).
As to claim 11:
Lenchner, Greaser, Lepeltier, and Vendrow discloses all of the limitations as set forth in claim 1 and some of 10 but does not appear to expressly disclose generate, via the ML chatbot, an email message to the select labeled email messages authors, inviting the select labeled email messages authors to connect with the employees associated with the one or more projects.
Lenchner also discloses:
The computer system of claim 10, further comprising training, via the one or more processors, the ML chatbot based upon historical data comprising: (i) independent variables comprising historical email messages [Paragraph 0060 teaches the data sources 401-405 may include, but are not limited to, data sources relating to one or more documents, materials related to emails. Paragraph 0066 teaches the NLP system 410 may include a content consuming component 411 for inputting the data sources 401-405 and running its NLP and AI tools against them, learning the content, such as by using the machine learning component 438. Paragraph 0069 teaches the immutable ledger 420 may also work in conjunction with an immutable ledger component 436 to maintain a timestamped record of all interactions and contributions of each content contributor to an intellectual property subject, topic, or idea.], and/or (ii) independent variables comprising descriptions of different projects, and/or (iii) dependent variables comprising associations between historical email messages and descriptions of different projects.
Claim(s) 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lenchner et al (U.S. Publication No.: US 20190114395 A1) hereinafter Lenchner, Greaser et al. (U.S. Publication No.: US 20220114340 A1) hereinafter Greaser, in view of Lepeltier (U.S. Publication No.: US 20170075877 A1) hereinafter Lepeltier, and further in view of Anisingaraju et al. (U.S. Publication No.: US 20160196511 A1) hereinafter Anisingaraju.
As to claim 12:
Lenchner, Greaser, and Lepeltier discloses all of the limitations as set forth in claim 1 and some of 10 but does not appear to expressly disclose wherein the one or more processors are further configured to: analyze, via the ML chatbot, the accessed one or more email messages to determine an employee satisfaction score.
Anisingaraju discloses:
The computer system of claim 1, wherein the one or more processors are further configured to: analyze, via the ML chatbot, the accessed one or more email messages to determine an employee satisfaction score [Paragraph 0098 teaches unstructured and structured data are analyzed to determine the evaluation target entity's score 1004 (current state) for each of the attributes. As an example, internal corporate datastores and/or employee evaluation and/or peer review database(s) may be analyzed to automatically determine the ETE's score for one or more scores for one or more of the satisfaction attributes. As another example, internal emails and memoranda by or pertaining to the ETE may be analyzed to obtain one or more scores for one or more of the satisfaction attributes for that ETE.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Lenchner, Greaser, and Lepeltier, by incorporating unstructured and structured data are analyzed to determine the evaluation target entity's score wherein the ETE score is based satisfactions attributes, as taught by Anisingaraju (see Paragraph 0098), because the four applications are directed to data analysis; incorporating unstructured and structured data are analyzed to determine the evaluation target entity's score wherein the ETE score is based satisfactions attributes improves the organization and/or analysis in the future (see Anisingaraju Paragraph 0122).
As to claim 13:
Lenchner, Greaser, Lepeltier, and Anisingaraju discloses all of the limitations as set forth in claim 1 and some of 10 but does not appear to expressly disclose training, via the one ormore processors, the ML chatbot based upon historical data comprising: (i) independent variables comprising historical texts.
Lenchner also discloses:
The computer system of claim 12, further comprising training, via the one or more processors, the ML chatbot based upon historical data comprising: (i) independent variables comprising historical texts [Paragraph 0060 teaches the data sources 401-405 may include, but are not limited to, data sources relating to one or more documents, materials related to emails. Paragraph 0069 teaches the immutable ledger 420 may also work in conjunction with an immutable ledger component 436 to maintain a timestamped record of all interactions and contributions of each content contributor to an intellectual property subject, topic, or idea. Paragraph 0066 teaches the NLP system 410 may include a content consuming component 411 for inputting the data sources 401-405 and running its NLP and AI tools against them, learning the content, such as by using the machine learning component 438.], and/or (ii) dependent variables comprising satisfaction rating associated with the authors and/or topics of the respective historical texts.
As to claim 14:
Lenchner, Greaser, Lepeltier, and Anisingaraju discloses all of the limitations as set forth in claim 1, 12, and 13.
Anisingaraju discloses:
The computer system of claim 13, wherein the satisfaction score is associated with at least a particular project, a particular team, or with a workplace [Paragraph 0098 teaches unstructured and structured data are analyzed to determine the evaluation target entity's score 1004 (current state) for each of the attributes. As an example, internal corporate datastores and/or employee evaluation and/or peer review database(s) may be analyzed to automatically determine the ETE's score for one or more scores for one or more of the satisfaction attributes. As another example, internal emails and memoranda by or pertaining to the ETE may be analyzed to obtain one or more scores for one or more of the satisfaction attributes for that ETE.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Lenchner, Greaser, and Lepeltier, by incorporating unstructured and structured data are analyzed to determine the evaluation target entity's score wherein the ETE score is based satisfactions attributes, as taught by Anisingaraju (see Paragraph 0098), because the four applications are directed to data analysis; incorporating unstructured and structured data are analyzed to determine the evaluation target entity's score wherein the ETE score is based satisfactions attributes improves the organization and/or analysis in the future (see Anisingaraju Paragraph 0122).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lenchner et al (U.S. Publication No.: US 20190114395 A1) hereinafter Lenchner, Greaser et al. (U.S. Publication No.: US 20220114340 A1) hereinafter Greaser, in view of Lepeltier (U.S. Publication No.: US 20170075877 A1) hereinafter Lepeltier, in view of Anisingaraju et al. (U.S. Publication No.: US 20160196511 A1) hereinafter Anisingaraju, and further in view of Dotan-Cohen et al. (U.S. Publication No.: US 20180048595 A1) hereinafter Dotan-Cohen.
As to claim 15:
Lenchner, Greaser, Lepeltier, and Anisingaraju discloses all of the limitations as set forth in claim 1, 12, and 13 but not appear to expressly disclose wherein the one or more email messages includes video data, and the ML chatbot extracts and analyzes employee facial expression from the video data.
Dotan-Cohen discloses:
The computer system of claim 13, wherein the one or more email messages includes video data, and the ML chatbot extracts and analyzes employee facial expression from the video data [Paragraph 0072 teaches the email messages or content may be modified by (a) summarizing the email content, which may include modifying the format of content (e.g., converting text to audio or generating a textual summary of non-textual content, such as an image, presentation, or video, which may be included in the body of the email message, as an attachment, or hyperlinked in the email message), and/or (b) providing a ranking of a set of email messages. Paragraph 0075 teaches automatic summarization also may be applied to non-textual content; for instance images or video maybe summarized based on image-feature extraction, such as object/facial recognition, OCR, metadata (including captioning).]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Lenchner, Greaser, Lepeltier, and Anisingaraju, by incorporating analyzing video data that is attached or included in the body of an email, as taught by Dotan-Cohen (see Paragraph 0072 and 0075), because the five applications are directed to data analysis; incorporating analyzing video data that is attached or included in the body of an email improve user efficiency and productivity on user devices available to the user (see Dotan-Cohen Paragraph 0122).
Claim(s) 16 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lenchner et al (U.S. Publication No.: US 20190114395 A1) hereinafter Lenchner, Greaser et al. (U.S. Publication No.: US 20220114340 A1) hereinafter Greaser, in view of Lepeltier (U.S. Publication No.: US 20170075877 A1) hereinafter Lepeltier, and further in view of Maikhuri et al. (U.S. Publication No.: US 20240054430 A1) hereinafter Maikhuri.
As to claim 16:
Lenchner, Greaser, and Lepeltier discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose determine, via the ML chatbot, a tone of the analyzed received email message and generate, via the ML chatbot, one or more responses to the analyzed received email message, at least one of the responses using the determined tone of the analyzed received email message.
Maikhuri discloses:
The computer system of claim 1, wherein the one or more processors are further configured to: analyze, via the ML chatbot, contents of a received email message from the accessed one or more email messages [Paragraph 0019 teaches text records associated with messages and/or chat records associated with communications with colleagues on project details, initiatives, innovation, etc.. Paragraph 0021 teaches text records associated with electronic mail message records, such as those related to one or more projects, initiatives, innovations, internal development plans, etc.]; determine, via the ML chatbot, a tone of the analyzed received email message [Paragraph 0024 teaches analyze the set of raw data records for at least one of sentiments, emotions, and intent.]; and generate, via the ML chatbot, one or more responses to the analyzed received email message, at least one of the responses using the determined tone of the analyzed received email message [Figure 11 and Paragraph 0178 teaches the message generation component 320, in certain embodiments uses a recurrent neural network (RNN) algorithm with LSTM. FIG. 11 is an example illustration of a context architecture diagram of the message generation component 320.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Lenchner, Greaser, and Lepeltier, by incorporating analyze the set of raw data records for at least one of sentiments, emotions, and intent, and generating a response based on that data, as taught by Maikhuri (see Paragraph 0019, 0021, 0024, Figure 11, and Paragraph 0178), because the four applications are directed to data analysis; incorporating analyze the set of raw data records for at least one of sentiments, emotions, and intent, and generating a response based on that data is advantageous (see Maikhuri Paragraph 0108).
As to claim 17:
Lenchner, Greaser, and Lepeltier discloses all of the limitations as set forth in claim 1.
Lenchner also discloses:
The computer system of claim 16, further comprising training, via the one or more processors, the ML chatbot based upon historical data comprising: (i) independent variables comprising historical texts [Paragraph 0060 teaches the data sources 401-405 may include, but are not limited to, data sources relating to one or more documents, materials related to emails. Paragraph 0066 teaches the NLP system 410 may include a content consuming component 411 for inputting the data sources 401-405 and running its NLP and AI tools against them, learning the content, such as by using the machine learning component 438. Paragraph 0069 teaches the immutable ledger 420 may also work in conjunction with an immutable ledger component 436 to maintain a timestamped record of all interactions and contributions of each content contributor to an intellectual property subject, topic, or idea.], and/or (ii) dependent variables comprising tone of the historical texts.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EARL LEVI ELIAS whose telephone number is (571)272-9762. The examiner can normally be reached Monday - Friday (IFP).
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/EARL LEVI ELIAS/ Examiner, Art Unit 2169
/SHERIEF BADAWI/ Supervisory Patent Examiner, Art Unit 2169
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