DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. 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.
3. Claims 1-19, filed on 3/13/2025, are pending in this office action.
Priority
4. Acknowledgment is made of applicant's claim for foreign priority based on an application filed in United Kingdom of Great Britain and Northern Ireland on 3/27/2024. It is noted, however, that applicant has not filed a certified copy of the GB24166709.6 application as required by 37 CFR 1.55. It is noted that the attempt by the office to electronically retrieve the foreign application GB24166709.6 has failed on 8/27/2025. Please file a certified copy of the priority document.
Information Disclosure Statement
5. Initialed and dated copy of Applicant’s IDs form 1449, filed 3/13/2025, is attached to the instant Office action.
Specification
6. The abstract of the disclosure is objected to because the abstract contains legal
terms (ie. “comprising”). Please remove legal terms. A corrected abstract of the
disclosure is required and must be presented on a separate sheet, apart from any other
text. See MPEP § 608.01(b).
Claim Objections
7. Claims 17 and 18 are objected to because of the following informalities:
Claims 17 and 18 are dependent on independent claim 1; however, the claims are written in a format of an independent claim. Claim 17 recites “A computer program product”. Claim 18 recite “An information processing apparatus”. Please amend the claims to recite a complete independent claim.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
8. 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.
9. Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 17 is directed towards "computer program”; however, computer program may reasonably be construed to include signals or carrier waves, as no language in the specification is recited that limits a computer program to statutory subject matter, and may then be reasonably interpreted as being embodied in non-statutory embodiments, such as carrier waves, wireless signals, and the like. While the specification states that “computer-readable media may include non-transitory computer-readable storage media” (page 26 line 4-5) and “The invention may be implemented as a
computer program or computer program product, i.e., a computer program tangibly embodied in a non-transitory information carrier,” (page 27 line 25-27), the computer program has not been limited to non-transitory computer program. In order to overcome this rejection, applicant may amend Claim 17 to positively recite a “non-transitory computer program”.
Claim Rejections - 35 USC § 103
10. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
11. Claim(s) 1-7 and 13-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gordon et al. (US Publication 2023/0186906 A1) in view of Michaeli et al. (US Publication 2023/0230613 A1).
As per claim 1, Gordon teaches A computer-implemented method comprising: (see Abstract)
obtaining real-time text data relating to a matter, the text data comprising a plurality of portions of information; (paragraph 0028, 0029, call data that contains transcripts of real-time phone calls are received by a text receiver, paragraph 0097, 0098, the calls being divided into portions)
performing a categorisation process, wherein the categorisation process is configured to run a plurality of threads in parallel, wherein each thread of the plurality of threads acts on one portion of information at a time, (paragraph 0031, 0036, 0042, 0045, the sentiment classification is iteratively determined based on analysis of sentences and generating a classification)
wherein each thread performs the following steps: obtaining a sentiment score based on the portion of information using a Sentiment Analysis machine learning, ML, model; (paragraph 0030, 0045, 0049, a sentiment is determined for sentences using a neural network based on a confidence value to determine sentiment)
assigning a category to the matter based on the sentiment score using a classification ML model trained on historical data; (paragraph 0030, 0049, 0068, the assigned sentiment is determined based on training data of previous sentences, paragraph 0095, 0098, call type and sentiment type are assigned based on confidence value and the trained neural network)
and updating a live category based on the category assigned to the matter; (paragraph 0104, changes to sentiment are stored)
and outputting the live category to a user (paragraph 0045, 0049, 0084, the assigned sentiments are output to render for display)
Gordon does not explicitly indicate outputting the live category to a user in real-time.
Michaeli teaches outputting the live category to a user in real-time. (paragraph 0066, 0071, 0087, an alert indicating classification of calls is provided upon detection of specific class, the alert with a classification interpreted as a live category for a call is received, paragraph 0175, 0177, the call information being real-time information).
It would have been obvious for one of ordinary skill in the art at the time the invention was made to combine Gordon’s method of performing sentiment analysis and classification for received call data with Michaeli’s ability to return alerts to users related to classification of calls. This gives the user the ability to return alerts to users in real-time when a sentiment is detected in a phone call. The motivation for doing so would be to perform analysis of received phone calls and determine if a distress call is a hoax (paragraph 0002).
As per claim 2, Gordon teaches the method further comprises receiving real-time voice data relating to the matter and converting the real-time voice data to the real-time text data. (paragraph 0028, 0038, call data store stores real time call data as text)
As per claim 3, Gordon teaches the matter comprises an event, an incident, a problem, a query or an inquiry. (paragraph 0098, query)
As per claim 4, Gordon and Michaeli are taught as per claim 3 above. Michaeli additionally teaches the matter comprises an emergency incident. (paragraph 0012, 0023, emergency distress call)
As per claim 5, Gordon and Michaeli are taught as per claim 3 above. Michaeli additionally teaches the real-time text data is derived from an emergency call. (paragraph 0012, 0023, emergency distress call)
As per claim 6, Gordon teaches the assigned category is a severity category indicating how severe and/or urgent the matter is. (paragraph 0062, weight operation)
As per claim 7, Gordon teaches outputting the live category to a user in real-time comprises displaying the live category to the user via a Graphical User Interface, GUI. (paragraph 0037, user interface)
As per claim 13, Gordon teaches the classification ML model trained on historical data is trained by: obtaining a historical database of historical data comprising a plurality of portions of text data each labelled with a category; and training the classification ML model based on the historical database. (paragraph 0030, 0045, trained neural network
As per claim 14, Gordon teaches labelling the text data with its assigned category and adding it to the historical database for training purposes. (paragraph 0030, 0045, labeled examples)
As per claim 15, Gordon teaches the Sentiment Analysis ML model takes the portion of information, and optionally the one or more keywords, as its inputs and outputs the sentiment score. (paragraph 0030, 0054, training using sentences as input)
As per claim 16, Gordon teaches the classification ML model takes the sentiment score, and optionally one or more keywords, as its inputs and outputs the category. (paragraph 0054, 0056, 0074, sentiment value utilized for classification)
As per claim 17, Gordon teaches A computer program which, when run on a computer, causes the computer to carry out the method of claim 1. (see claim 1; Additionally paragraph 0060, 0076, computer readable medium)
As per claim 18, Gordon teaches An information processing apparatus comprising a memory and a processor connected to the memory, wherein the processor is configured to perform the method of claim 1. (see claim 1; Additionally paragraph 0060, 0076, system with memory and processor)
As per claim 19, Gordon teaches the memory and the processor are collectively configured to provide a Parallel Event Categorisation, PEC, module arranged to perform the categorisation process, wherein the PEC module comprises the plurality of threads and each thread comprises the Sentiment Analysis ML model and the classification ML model, and optionally the NER ML model. (paragraph 0031, 0036, 0042, 0045, the sentiment classification is iteratively determined)
Allowable Subject Matter
12. The following is a statement of reasons for the indication of allowable subject matter:
Claims 8-12 contain allowable subject matter over the prior art of record because the prior art of record fails to teach or fairly suggest each thread additionally performs a step of obtaining one or more keywords from the portion of information using a Name Entity Recognition, NER, ML model, as disclosed in claim 8. Specifically the prior art of Gordon in view of Michaeli teaches performing sentiment analysis and classification for emergency distress calls and returning alerts when detecting specific classifications, but does not explicitly indicate determining keywords based on a Name Entity Recognition machine learning model in each of the parallel threads.
Claim 8-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Knudson (US Publication 2024/0412048 A1)
Guzik (US Publication 2022/0171969 A1)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANGELINO N GORTAYO whose telephone number is (571)272-7204. The examiner can normally be reached Monday-Friday 7:00am - 3:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached at 571-272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DANGELINO N GORTAYO/Primary Examiner, Art Unit 2168