DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
The following is a final office action.
Claims [1-4, 6-13, and 15-20] are currently pending and have been examined.
Claims 1, 4, 6-7, 9-10, 13, 15-16, and 18-19 are currently amended see REMARKS February 27, 2025.
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 1-4, 6-13, and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception that is an abstract idea without a practical application or significantly more.
Step 1: Claims 1-4 and 6-9 recite a system, claims 10-13 and 15-18 recite a method (i.e. a series of steps), and claims 19-20 recite a non-transitory computer readable medium, and therefore each claim falls within one of the four statutory categories.
Step 2A prong 1 (Is a judicial exception recited?):
The representative claims 1, 10, and 19 recite: A method comprising: generating, user data corresponding to a plurality of users in an electronic meeting (e-meeting), based on meta data received from one or more data sources, wherein the generated user data is stored in a data lake; retrieving, from the data lake, the user data corresponding to the plurality of users in the e-meeting, wherein each of the plurality of users has a corresponding user profile; generate a probable aspiration dataset by determining, from the user data, factual user information for each user of the plurality of users from the corresponding user profile, based on skills data and technical domains of corresponding user, wherein the factual user information for each user is determined by: pre-processing the user data to generate pre-processed data suitable for training the random forest models using a data cleansing process, wherein the user data is pre-processed to detect outliers and imbalanced data and filter the detected outliers data and process the imbalanced data; and generating, the probable aspiration dataset by: determining, for each user of the plurality of users, the factual user information from the corresponding user profile, wherein the factual user information is determined by predicting an aspiration; and assigning the aspiration to a category that receives a majority vote from the predictions; determine, the aspiration of the user of the plurality of users based on feed of the user data of the user to the probable aspiration dataset; comparing, the factual user information of the plurality of users based on the aspiration of the user, to determine a set of commonalities between the plurality of users; generating, for each user, integration data based on the comparison of the factual user information and the set of commonalities; and (presenting), for each user, a plurality of meeting objects, based on the integration data, to provide one or more profile services.
The claims recite a certain method of organizing human activity. The claims recite a certain method of organizing human activity as the disclosure is directed to managing personal behavior or relationships or interactions between people. The claims recite a series of steps for generating user data corresponding to a plurality of users in a meeting and determining and comparing factual user information of the plurality of users to determine a set of commonalities between the users and presenting one or more profiles. Therefore, the claims recite an abstract idea as they are a series of steps for matching users in a meeting based on common characteristics to help connect users. Merely performing a series of steps to generate a profile of a user based on their characteristics and commonalities between other members of a meeting is an abstract idea.
The examiner further finds that the claims are directed to a mental process. The claims recite a method of generating user data, retrieving the user data, determining factual user information, comparing the factual user information of a plurality of users to determine commonalities, and generate integration data to provide one or more profiles to users in a meeting. The claims therefore, recite a mental process as a person is capable of performing a series of steps of determining characteristics of participants in a meeting and commonalities amongst different people to connect people in their mind or by using simple tools such as pen and paper. As these steps are capable of being performed by someone such as a host of a meeting or a common friend in a meeting who is capable of connecting individuals based on known commonalities. Additionally, the claims recite steps and procedures that are similar to concepts the courts have identified as a mental process such as observations, evaluations, judgements, and opinions. Therefore, the claims recite an abstract idea.
Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite;
Claim 1: A system comprising: a processor; a memory coupled to the processor, wherein the memory comprises processor- executable instructions and a meeting interface associated with the e-meeting; a trained engineering machine learning model; converting audio files corresponding to speech to uniform dimensions, wherein thetraining the plurality of random forest models using the pre-processed data, wherein training comprises: selecting random subsets of K data points from the pre-processed data; generating decision trees associated with the selected random subsets of the K data points; determining a number N of decision trees to be generated for each random forest model; repeating steps of selecting random subsets and generating decision trees until N decision trees are generated for each random forest model; using the trained random forest models, obtaining predictions from each decision tree in the random forest models, and displaying on a graphical user interface.
Claim 10: A processor associated with a system and a meeting interface associated with the e-meeting, a trained engineering machine learning model; converting audio files corresponding to speech to uniform dimensions, wherein thetraining the plurality of random forest models using the pre-processed data, wherein training comprises: selecting random subsets of K data points from the pre-processed data; generating decision trees associated with the selected random subsets of the K data points; determining a number N of decision trees to be generated for each random forest model; repeating steps of selecting random subsets and generating decision trees until N decision trees are generated for each random forest model; using the trained random forest models, obtaining predictions from each decision tree in the random forest models, and displaying on a graphical user interface.
Claim 19: A non-transitory computer-readable medium comprising machine-readable instructions that are executable by a processor and a meeting interface associated with the e-meeting, a trained engineering machine learning model; converting audio files corresponding to speech to uniform dimensions, wherein the training the plurality of random forest models using the pre-processed data, wherein training comprises: selecting random subsets of K data points from the pre-processed data; generating decision trees associated with the selected random subsets of the K data points; determining a number N of decision trees to be generated for each random forest model; repeating steps of selecting random subsets and generating decision trees until N decision trees are generated for each random forest model; using the trained random forest models, obtaining predictions from each decision tree in the random forest models, and displaying on a graphical user interface.
The additional element of generic computer elements and generic machine learning elements to perform the abstract idea of generating user data, retrieving user data, determining factual user information, comparing user information, and displaying user information are directed to mere instructions to apply a generic computer and technology to execute the method in the recited claim limitations, as merely using a computer platform to transmit, display, and manipulate information is not an improvement to a technology or technical field. Therefore, the limitations merely amount to adding the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The additional elements of training and using a plurality of random forest models to perform the abstract idea are directed to merely “apply it” or applying generic random forest model techniques to perform the abstract idea of receiving user data, comparing user data to other users to determine commonalities between the users, and presenting the information to a user. The claims do not recite an improvement to a technology or technical field of random forest models but merely using or applying a model to the abstract idea. Therefore, the additional elements do not integrate the claims into a practical application.
The additional elements of “converting audio files corresponding to speech to uniform dimensions, wherein the audio files are converted to the uniform dimensions by: resampling the audio files such that each of the audio files are sampled at a same sampling rate; padding shorter sequences of the audio files and truncating longer sequences of the audio files to convert each of the audio files to a same duration; and applying a noise-removal technique to eliminate background noise from one or more of the audio files to enhance poor audio quality of the one or more audio files, converting the audio files with the uniform dimensions into a Mel spectrogram, to determine attributes of the speech in the audio files, converting the Mel spectrogram to Mel Frequency Cepstral Coefficients (MFCC) to determine frequency coefficients of the speech, and converting the frequency coefficients to the text” are directed extra-solution activity as the additional elements merely recite mere data gathering to be used in the abstract idea. The claims recite an abstract idea of generating user data corresponding to a plurality of users in a meeting, retrieving user data, determining factual user information, compare the factual user information, generate integration data comprising the factual user information and the set of commonalities and present for each user a plurality of meeting objects in a meeting. The claims merely recite a method for retrieving user information about participants in a meeting, comparing the information, and generating and presenting factual information to individuals with commonalities in a meeting. The additional element of converting audio files to text is merely a step to generate a type of user data that could be used in the rest of the claims but does not necessarily generate data that would be required. Therefore, the additional elements are directed to mere extra-solution activity or generating data that could potentially be used in the invention.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?): As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The additional elements do not recite an improvement to a technology or technical field but merely utilize the generic computer elements to perform the abstract idea of determining commonalities between users in a meeting and presenting data to members of a meeting. Therefore, the additional elements do not direct the claims to significantly more.
The additional elements to convert audio files by performing a series of calculations such as converting audio files with uniforms dimensions into a Mel spectrogram are well known, routine, and convention. As the conversion of a frequency of an audio file to a Mel spectrogram is done by applying a known mathematical equation “mel = 1/log(2) * (log(1 + (Hz/1000))) * 1000.” (see Abdul, Zrar Kh, and Abdulbasit K. Al-Talabani. "Mel frequency cepstral coefficient and its applications: A review." IEEE Access 10 (2022): 122136-122158). Therefore, the additional elements do not amount to significantly more.
The dependent claims 2-9, 11-18, and 20 further narrow the abstract idea of determining user data for participants of a meeting, comparing user information, and generating a plurality of meeting objects to provide one or more profiles, recited in the independent claims 1, 10, and 19 and are therefore directed towards the same abstract idea.
The dependent claims recite the following additional elements:
Claims 6 and 15 recite: professional network websites
However, the additional elements are directed to merely “apply it” or being applied to perform the abstract idea.
Therefore, claims 1-4, 6-13, and 15-20 are rejected under 35 U.S.C. 101.
Response to arguments
Applicant’s arguments, see REMARKS, filed July 18, 2025, with respect to the rejections of claims 1-4, 6-13, and 15-20 under U.S.C. 101 have been fully considered but are not persuasive.
Representative argues that the currently amended claims no not recite an abstract idea as they recite claim limitations of converting audio to text by performing operations such as resampling the audio file, padding shorter sequences of the audio files, and truncating longer sequences, applying a noise-removal technique, converting the audio files with uniform dimensions into a Mel spectrogram, converting the Mel spectrogram to Mel frequency cepstral coefficients, and converting the frequency coefficients to the text; aw well as pr-processing user data to generate pre-processed data suitable for training the random forest models using a data cleansing process, wherein the user data is pre-processed to detect outliers and imbalanced data and filter the detected outliers data and process the imbalanced data; training the plurality of random forest models using the pre-processed data; generating decision trees associated with the selected random subset of the K data points; determining a number of decision trees to be generated; generating using the trained random forest models the probable aspiration dataset by determining the factual user information form the corresponding user profile, and assigning the aspiration to a category that receives a majority vote from the decision trees, which cannot be practically performed in the human mind. However, the examiner respectfully disagrees as the claims recite the claim limitations of generating user data corresponding to a plurality of users in a meeting based on meta data received from one or more sources, retrieving the user data corresponding to the plurality of users in the meeting, wherein each of the plurality of users has a corresponding user profile, determining from the user data factual user information for each user of the plurality of users from the corresponding user profile, wherein the factual user information comprises an aspiration that is determined based on skills data and technical domains of corresponding users, pre-processing user data to generate pre-processed data suitable for training a random forest model using a data cleansing process, generating the probable aspiration dataset by determining for each user of the plurality of users the factual user information from the corresponding user profile, assigning the aspiration to a category, compare the factual user information of the plurality of users to determine a set of commonalities between the plurality of users, generate for each user integration data comprising the factual user information and a set of commonalities, and presenting for each user a plurality of meeting objects based on the integration data, to provide one or more profile services. The claims recite a series of steps for generating user data based on meta data such as a user’s personality, aspirations, skills, hobbies, interests, etc. And then using that information during a comparison between a plurality of people in a meeting to determine commonalities between the participants and presenting the commonalities to the individuals. The claims further recite merely processing user information to generate training data and predicting aspirations of a user based on the processed user data. The claims recite a mental process as these steps would be capable of being performed mentally or using simple tools such as pen and paper by a person, such as someone leading a meeting and trying to get individuals in a meeting to know one another and interact based on their shared interests. As a person would mentally be able to receive or know information about participants of a meeting, compare their individual information, and present that information to each person to facilitate connects. A person is also capable of mentally or with simple tools such as pen and paper performing data processing to generate training data for a model as well as determine factual user information from a user profile by predicting aspirations based on user data received from a plurality of sources. Alternatively, the claims recite a certain method of organizing human activity as they recite a method of managing personal behavior or interactions between people. The claims recite a series of steps to facilitate the interactions of meeting participants by gathering, analyzing, and presenting information pertaining to meeting participants to facilitate connections. Therefore, the claims recite an abstract idea.
The representative argues that the additional elements of the amended claim limitations integrate the claims into a practical application. The applicant argues that the additional elements recite an improvement in providing a precise and accurate speech to text conversion. However, the examiner respectfully disagrees as the additional elements merely recite generic computer elements to perform the abstract idea by performing generic computer actions such as generating, storing, and retrieving information as well as performing an analysis of the information by determining user data, comparing the information, generating integration data, and displaying the information in a display. These additional elements do not recite an improvement to a technology or technical field but are directed to merely “apply it” or applying generic computer elements to perform standard steps of receiving, storing, analyzing, and displaying data. Furthermore, the additional elements of generating speech to text converted data is directed to extra-solution activity. As the claims merely recite a process for generating speech to text converted data by performing a well-understood, routine, and conventional process of converting audio files to speech with uniform dimensions by performing the steps of re-sampling the audio file padding shorter sequences, truncating longer sequences, and applying noise removal techniques, as well as converting the audio files with uniform dimensions into a Mel spectrogram, converting the Mel spectrogram to Mel frequency cepstral coefficients to determine frequency coefficients of the speech, and converting the frequency coefficients to the text. Furthermore, the generating text data is merely a part of the potential user data that could be utilized in the recited claim limitations. As the claims do not need to use the generated text data from audio files to perform the inventive concept of retrieving user data, determining from the user data factual user information, comparing the factual user information to determine a set of commonalities, and generating for each user integration data comprising the factual user information and the set of commonalities. Therefore, the additional elements of the amended claim limitations are directed to merely extra-solution activity. The applicant further argues that the claims are directed to a practical application and significantly more as the claims recite pre-processing data to train random forest models and using random forest models to generate the probable aspiration dataset. However, the examiner respectfully disagrees as the claims merely recite “apply it” or applying generic random forest model techniques to perform the abstract idea of receiving and processing user data to generate factual user information from user profiles by predicting an aspiration and assigning the aspiration to a category. Merely using a random forest model to perform the generic process of receiving and processing user data to generate factual user information to be used in the abstract idea of receiving and comparing user information to determine a set of commonalities between members of a meter is not an improvement to a technology or technical field. The claims do not recite an improvement to the technology or random forest models but using the models to perform the abstract idea of receiving and processing user data from a plurality of sources. Therefore, the claims are not directed to a practical application. Additionally, as the claims are directed to merely “apply it” they do not amount to significantly more.
Therefore, the examiner maintains the current 101 rejection.
Claims 2-4, 6-9, 11-13, 15-18, and 20 are dependent on claims 1, 10, and 19 and therefore are rejected under the same rejection.
Applicant’s arguments, see REMARKS, filed July 18, 2025, with respect to the rejections of Claim(s) 1-4, 6-13, and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lagares-Greenblatt (US 2020/0193389) in view of Mazzoccoli (US 2022/0238118) further in view of Duggal (US 2022/0215834) even further in view of Neckermann (US 2022/0138697) are persuasive, as the prior art is not found to found to disclose the newly amended claim limitations.
Claims 1, 10, and 19: The representative argues that the current combination of prior art does not disclose the newly amended claim limitations of “wherein the factual user information for each user is determined by: pre-processing the user data to generate pre-processed data suitable for training the random forest models using a data cleansing process, wherein the user data is pre-processed to detect outliers and imbalanced data and filter the detected outliers data and process the imbalanced data; training the plurality of random forest models using the pre-processed data, wherein training comprises: selecting random subsets of K data points from the pre-processed data; generating decision trees associated with the selected random subsets of the K data points; determining a number N of decision trees to be generated for each random forest model; repeating steps of selecting random subsets and generating decision trees until N decision trees are generated for each random forest model; and generating, using the trained random forest models, the probable aspiration dataset by: determining, for each user of the plurality of users, the factual user information from the corresponding user profile, wherein the factual user information is determined by predicting an aspiration by obtaining predictions from each decision tree in the random forest models; and assigning the aspiration to a category that receives a majority vote from the predictions of the decision trees.”
The examiner agrees that the combination of prior art does not disclose the newly amended claim limitations.
The closes prior art Lagares-Greenblatt (US 2020/0193389) discloses a system of providing insights appropriate to attendees of a web-event or meeting (Lagares-Greenblatt [0004]). To accomplish this the system generates information about the attendees by performing a background check through public information sources such as social networks (Lagares-Greenblatt [0018]). The system can then determine traits and past activities of a user, such as projects they have worked on, interests, work experience, and relationships (Lagares-Greenblatt [0041]).
The second closes prior art Neckermann (US 2022/0138697) teaches a system of using a plurality of machine learning techniques such as decision trees (Neckermann [0047]) to generate knowledge graph representations of user information such as user interests and traits (Neckermann [0006]). Neckermann further teaches identifying user specific insights and determining similar traits between users (Neckermann [0036]). Neckermann teaches a system of using a machine learning model such as a random forest model to determine aspiration or traits of individuals based on contextual information to identify similarities between users.
The third closest prior art Mazzoccoli (US 2022/0238118) teaches an apparatus for processing audio signals to generate speech transcription (Mazzoccoli [0010]). Mazzoccoli further teaches resampling audio files to generate uniform audio samples when generating transcripts of speech (Mazzoccoli [0071]).
The fourth closest prior art is Duggal (US 2022/0215834) which teaches a system of generating text from speech and removing background noise (Duggal [0047]).
However, the combination of prior art does not disclose the newly amended claim limitations of “wherein the factual user information for each user is determined by: pre-processing the user data to generate pre-processed data suitable for training the random forest models using a data cleansing process, wherein the user data is pre-processed to detect outliers and imbalanced data and filter the detected outliers data and process the imbalanced data; training the plurality of random forest models using the pre-processed data, wherein training comprises: selecting random subsets of K data points from the pre-processed data; generating decision trees associated with the selected random subsets of the K data points; determining a number N of decision trees to be generated for each random forest model; repeating steps of selecting random subsets and generating decision trees until N decision trees are generated for each random forest model; and generating, using the trained random forest models, the probable aspiration dataset by: determining, for each user of the plurality of users, the factual user information from the corresponding user profile, wherein the factual user information is determined by predicting an aspiration by obtaining predictions from each decision tree in the random forest models; and assigning the aspiration to a category that receives a majority vote from the predictions of the decision trees.”
Therefore, claims 1, 10, and 19 are allowable over U.S.C. 103.
The representative argues that claims 2-4, 6-9, 11-13, 15-18, and 20 are allowable as being dependent on claims 1, 10, and 19. Therefore, they are also allowable over U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Vijayakumar (US 2011/0022967) Context enhanced social network for meeting participants.
Seger (US 2016/0147810) Recommending users to social networking system user based on relevance and similarity between users.
Dalmia (US 10140973) Text-to-speech processing using previously speech processed data.
Shah (US 2021/0312312) Similarity learning-based device attribution.
Nanos, Antonios G., and Anne E. James. "A virtual meeting system for the new age." 2013 IEEE 10th International Conference on e-Business Engineering. IEEE, 2013.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/COREY RUSS/Examiner, Art Unit 3629