Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rangan (20170039527) in view of Peters et al (20210076002) in view of Choi et al (20150331856).
As per claim 1, Rangan (20170039527) teaches a method comprising: training an attendee matchmaking algorithm, by:
inputting, into a computer system, attendance data from multiple past events, registration data from the multiple past events, and session data from the multiple past event; (see para 0041, utilizing a cloud-resident service tracking/monitoring information in meeting; then, classifying all the participants in the meeting; and tracking registration data, in the form of emails, product linkages, new article linkages, etc
– “ b) Automatically classify the participants as internal or external, using both heuristics as well as machine learning based on past data. In one embodiment, for each participant, an email address in a meeting invitation can be used to identify the domain of the email. The domain can then be matched against a map of domain-to-accounts.
c) For each external participant, identify their LinkedIn information and extract their current role, as well as the role that established a connection with them.
d) Formulate searches of the sales professional's email and other information repositories with both keywords from the meeting as well as from other emails that these participants are involved in.
e) Extract specific records from enterprise applications, e.g., Salesforce, that the sales professional has access to that are relevant to the specific customer prospect. The cloud-resident service can identify records as either notes from prior meetings as well as whether they are critical items for a successful meeting.
f) Search similar records from other sales professional's for the same product. Also, the cloud-resident service can gather any prior history with other products with the customer.
g) Search the public web for news articles for the customer.
h) Search public company databases such as Jigsaw to identify competitors that sell similar products.
i) Search public forums where competitive products are discussed to get a sentiment for these products.”
executing, via a processor of the computer system, a sensitivity analysis on the attendance data, the registration data, and the session data, resulting in relevance coefficients; and generating, via the processor, the attendee matchmaking algorithm using the relevance coefficients (see para [0056], "Content analytics component 435 analyzes content to build business interest graph 415. Business interest graph 415 maintains relationships between users and content and is able to answer questions such as the group of users that have similar content interest, as well as groups of content that is accessed in response to a task or information request. Content analytics component 435 can incorporate various machine learning components such as collaboration filtering algorithms, support vector machine based classifier, clustering algorithms (canopy clustering and kMeans clustering), kNN nearest neighbor algorithm and singular value decomposition algorithm for eliminating noise in the data.");
“receiving, during an ongoing event (para 0151 showing real-time events or realtime event updates) via the processor, audio recorded in real-time at a session of the ongoing event by a mobile computing device of an attendee at the session; executing, via the processor, natural language processing algorithms to extract one or more key phrases susbsequent to receiving recorded audio by the mobile computing device, resulting in processed audio” comprising the one or more
with processing the audio -- see para [0158], "1/0 subsystem 1808 may include user interface input devices and user interface output devices. User interface input devices may include a keyboard, pointing devices such as a mouse or trackball, a touch pad or touch screen incorporated into a display, a scroll wheel, a click wheel, a dial, a button, a switch, a keypad, audio input devices with voice command recognition systems, microphones, and other types of input devices. User interface input devices may include, for example, motion sensing and/or gesture recognition devices such as the Microsoft Kinect motion sensor that enables users to control and interact with an input device, such as the Microsoft Xbox 360 game controller, through a natural user interface using gestures and spoken commands. User interface input devices may also include eye gesture recognition devices such as the Google Glass blink detector that detects eye activity (e.g., 'blinking' while taking pictures and/or making a menu selection) from users and transforms the eye gestures as input into an input device (e.g., Google Glass). Additionally, user interface input devices may include voice recognition sensing devices that enable users to interact with voice recognition systems (e.g., Siri navigator), through voice commands."; Audio-based commands are processed);
“executing, via the processor, the attendee matchmaking algorithm with the processed audio as an input,
Analyzing, during the execution of the attendee matchmaking algorithm, one or more activites of the attendee
As per claim 1, Rangan (20170039527) teaches identifying commands using natural language processing (see para 0158), but does not explicitly teach the detail of key phrases; Peters et al (20210076002) teaches a system that analyzes the actions of participants (abstract), a scoring module (train machine learning model) analyzing facial expression, eye gaze, gesture recognition, and speech recognition (fig. 2b), in real-time – para 0110, recognizing keywords and topics as spoken by the user – para 0012, 0022. Therefore, it would have been obvious to one of ordinary skill in the art of processing live/real time events to modify the system of Rangan (20170039527) with real-time audio recognition, as taught by Peters et al (20210076002) , because it would advantageously allow the possibility of facilitating more engagement, among the conference participants, by analyzing what is being said by the participants (see Peters et al (20210076002), para 0066).
At this juncture, the combination of Rangan (20170039527) in view of Peters et al (20210076002) teaches the general concept of gathering user’s feedback on a recorded event, and updates/real-time updates relating to real-time events – see Rangan (20170039527), para 0151; however, the combination of Rangan (20170039527) in view of Peters et al (20210076002) does not explicitly teach that the attendee is at the particular event, live, as the event is happening (Rangan (20170039527) , as explained above, does teach the concept of gathering reactions to the live-event live-streamed event, etc., -- see further in para 0169, 0170). Choi et al (20150331856) teaches the idea of user submitting images/video/text content,etc., by users that are at the live event – see para 0030, in view of para 0003 showing content that is recorded in real-time at the event. Therefore, it would have been obvious to one of ordinary skill in the art of sharing user commentary/etc on live events to modify the system of Rangan (20170039527) in view of Peters et al (20210076002) with attendees recording and providing their feedback, at the actual live event, as taught by Choi et al (20150331856) because it would advantageously allow for users to influence content generation related to the event, as it is happening in real time (see Choi et al (20150331856), para 0096).
As per claim 2, the combination of Rangan (20170039527) in view of Peters et al (20210076002) in view of Choi et al (20150331856) teaches the method of claim 1, wherein the sensitivity analysis for the attendee matchmaking analysis further receives, as inputs: topics of interest by the attendee; and people with whom the attendee spoke at the multiple past events (as, Rangan (20170039527), topic indexes of the extracted data from the meetings – para 0064; and spoken-to-attendees – para 0008, 0115 – the attendee of the meeting, via calendar, teaches “spoken to”).
As per claim 3, the combination of Rangan (20170039527) in view of Peters et al (20210076002) in view of Choi et al (20150331856) teaches the method of claim 1, wherein the ranked suggestions comprise:
at least one of:
an individual with whom the attendee is recommended to communicate, a session of the event the attendee is recommended to attend, an exhibitor booth the attendee is recommended to visit, a web page the attendee is recommended to visit, and a meeting of multiple individuals which the attendee is recommended to join (as, Rangan (20170039527), recommended list of opportunities for the attendees – para 0131, and fig. 16a-16g, showing opportunities/recommendations – para 0132-0136).
As per claim 4, the combination of Rangan (20170039527) in view of Peters et al (20210076002) in view of Choi et al (20150331856) teaches the method of claim 1, wherein the sensitivity analysis comprises at least one of: a derivative-based local method, a regression analysis, a variance-based method, and scatter plots (as, Rangan (20170039527), See fig. 14, with cross probability values; fig. 15B calculating convergent measures; see also para 0127 – 0130).
As per claim 5, the combination of Rangan (20170039527) in view of Peters et al (20210076002) in view of Choi et al (20150331856) teaches the method of claim 1, further comprising:
“receiving location coordinates for future activities at the ongoing event; and receiving global positioning system (GPS) locations for the attendee, wherein the ranked suggestions are filtered based on a distance between the location coordinates for future activities and the GPS locations for the attendee.” (as, Rangan (20170039527), see para 0095, "The cloud system can incorporate multiple models of delivery of curated content to individual users. The cloud system can rely on modern data networks and the ability to monitor individual user location, their tasks, and the devices they use to consume data. In one example, curated content can be delivered on specially prepared dynamic web page, which can be accessed by individuals using a modern browser. Content can be made available specifically for mobile devices using device-specific native applications, designed for that device. Curated content can utilize simple navigation paradigms, layout and tap and touch sensitive UI, and other features that make for a pleasing and highly interactive end user application appropriate for the device. In order to facilitate offline content consumption, the content cam be packaged in independent and self-contained containers, such as PDF files."; para [0167], "Communications subsystem 1824 provides an interface to other computer systems and networks. Communications subsystem 1824 serves as an interface for receiving data from and transmitting data to other systems from computer system 1800. For example, communications subsystem 1824 may enable computer system 1800 to connect to one or more devices via the Internet. In some embodiments communications subsystem 1824 can include radio frequency (RF) transceiver components for accessing wireless voice and/or data networks {e.g., using cellular telephone technology, advanced data network technology, such as 3G, 4G or EDGE {enhanced data rates for global evolution), WiFi {IEEE 802.11 family standards, or other mobile communication technologies, or any combination thereof), global positioning system (GPS) receiver components, and/or other components. In some embodiments communications subsystem 1824 can provide wired network connectivity {e.g., Ethernet) in addition to or instead of a wireless interface).
As per claim 6, the combination of Rangan (20170039527) in view of Peters et al (20210076002) in view of Choi et al (20150331856) teaches the method of claim 1, further comprising: receiving additional actions of the attendee after receiving the ranked suggestions; and modifying the attendee matchmaking algorithm based on the additional actions (as, Rangan (20170039527), See para 0111, wherein the ranking is based on people, tasks, content, and indexing/normalization).
As per claim 7, the combination of Rangan (20170039527) in view of Peters et al (20210076002) in view of Choi et al (20150331856) teaches the method of claim 1, wherein each suggestion in the ranked suggestions comprises: at least one of a person to meet and a session to attend; and a reason for the suggestion (as, Rangan (20170039527), see para 0111, 0112 teaches generation of, one or more recommended actions, with the reasoning being, importance of the author of the context).
Claims 8-14 are system claims that perform the method steps found in claim 1-7 above and as such, claims 8-14 are similar in scope and content to claims 1-7 above; therefore, claims 8-14 are rejected under similar rationale as presented against claims 1-7 above. Furthermore, Rangan (20170039527) teaches a processor/memory performing the steps – para 0137. Further to claim 8, Rangan (20170039527) teaches a processor in a handheld devices (para 0146, as part of the client computing systems).
Claims 15-20 are non-transitory computer readable medium claims storing instructions executed by a processor that perform the method steps found in claim 1-7 above and as such, claims 15-20 are similar in scope and content to claims 1-7 above; therefore, claims 15-20 are rejected under similar rationale as presented against claims 1-7 above. Furthermore, Rangan (20170039527) teaches a processor/memory performing the steps – para 0137. Further to claim 17, Rangan (20170039527) teaches a processor in a handheld devices (para 0146, as part of the client computing systems).
Response to Arguments
Applicants arguments filed 06/24/2026 have been fully considered but are moot in view of the new grounds of rejection. Examiner notes the introduction of the Choi et al (20150331856) reference teaching, at the live event, the attendees gathering information/comments and processing/analysis of participants speech/video/gestures to facilitate further interaction among conference participants.
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see related art listed on the PTO-892 form.
Furthermore, the following references were found to be pertinent to applicants spec and certain claim features:
Burton (20210169417) teaches recording live event/adding commentary, and downloading – para 0233.
Mombourquette et al (20210058436) teaches monitoring of live collaboration sessions (para 0021) and performing user analysis based on their speech to determine distracting type conversations – para 0047,
Castelli et al (20200090659) teaches personalization of content for a teleconference based on what listeners are interested in – para 0004, 0012, 0021, 0022)
Bettencourt-Silva et al (20200143273) teaches the use of data analytics (para 0061) to suggest events/meeting based on shared interests – para 0016, 0017).
Pinckney et al (20130124449) teaches narrowing of content via topics (para 0118-0119) using heuristics of the users information (para 0199).
Andreou (20180315076) teaches management of content/topics based on shared interest of users derived from messages – para 0037.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Opsasnick, telephone number (571)272-7623, who is available Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Richemond Dorvil, can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael N Opsasnick/Primary Examiner, Art Unit 2658 09/25/2026
