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 § 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.
The claims recite a method, system and a non-transitory computer readable medium; therefore, the claims pass step 1 of the eligibility analysis.
For step 2A, the claim(s) recite(s) an abstract idea of generating an incident status report for an incident (the incident can be any type of incident).
Using claim 1 as a representative example that is applicable to claims 8, 15, the abstract idea is defined by the elements of:
obtaining a set of reportable criteria related to the incident;
identifying, using one or more classifiers, a subset of the reportable criteria described in the incident data;
[Note: the claimed classifiers of the independent claims can be human beings who are reading and identifying reportable criteria from the incident data]
transmitting a summarization request, wherein the summarization request includes the subset of the reportable criteria and the incident data; and
transmitting an incident status report received in response to the summarization request to one or more users
The generation of an incident status report is the act of keeping updating others about the status of an incident, where the incident can be anything. This is something that is considered to be a certain method of organizing human activities type of abstract idea. Human beings can perform the claimed steps. When an incident occurs, such as a law enforcement incident (a crime) or an accident, it is known that people track the status of the incident and can received updates about the status, such as whether or not the incident is taken care of or is still in progress, etc.. This is considered to be managing relationships between people by keeping interested parties informed about the status of an incident. The claimed concept is similar to the President receiving status updates about a military operation that is in progress, as was done in WWII, or a CEO obtaining an update about a data breach that has occurred. The concept of obtaining incident data and providing an incident status report that includes information about the incident and reportable data is something that people can do and is reciting human activity. For this reason the claims are considered to be reciting a certain method of organizing human activity.
For claim 1 the additional elements are the recitation to a language model. The language model has been interpreted as being a model such as an artificial intelligence model that is being used to create the incident status report that is itself part of the abstract idea.
For claim 8, the additional elements are the one or more memories, one or more processors and the recitation to a language model.
For claim 15, the additional elements are the non-transitory computer readable medium that is storing instructions to perform the steps that define the abstract idea, and the use of the language model.
This judicial exception is not integrated into a practical application (2nd prong of eligibility test for step 2A) because the additional elements of the claim when considered individually and in combination with the claim as a whole, amount to the use of a computing device (with a processor(s) and memory) that is being merely used as a tool to execute the abstract idea, in combination with a general link to the use of language models, see MPEP 2106.05(f), (h). The claim is simply instructing one to practice the abstract idea by using a generically recited computing devices that have a processor and memory. This is claiming computer implementation for the abstract idea and does not provide for integration into a practical application, see MPEP 2106305(f). Using a language model to output an incident report is a general link to the particular field of machine learning for execution of a step that defines the abstract idea, see MPEP 2106.05(h). The use of the language model is broadly reciting the use of artificial intelligence models to provide a summary report to a user in the form of an incident report. The extent of the use of the processors and memory and the language model is that they are being used as a tool to execute the abstract idea. This does not amount to more than a mere instruction to implement the abstract idea on a computer that uses a language model in the form of an artificial intelligence model. This is the equivalent of reciting “apply it” with a computer for the abstract idea and is taken as a link to a particular technological environment, that is the use of computers and artificial intelligence (machine learning). This is indicative of the fact that the claim has not integrated the abstract idea into a practical application and therefore the claim is found to be directed to the abstract idea identified by the examiner.
For step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception when considered individually and in combination with the claim as a whole because they amount to the use of a computing device (with a processor(s) and memory) that is being merely used as a tool to execute the abstract idea, in combination with a general link to the use of language models, see MPEP 2106.05(f), (h). The claim is simply instructing one to practice the abstract idea by using a generically recited computing devices that have a processor and memory. This is claiming computer implementation for the abstract idea and does not provide for integration into a practical application, see MPEP 2106305(f). Using a language model to output an incident report is a general link to the particular field of machine learning for execution of a step that defines the abstract idea. The use of the language model is broadly reciting the use of artificial intelligence models to provide a narrative to a user in the form of an incident report. The extent of the use of the processors and memory and the language model is that they are being used as a tool to execute the abstract idea. This does not amount to more than a mere instruction to implement the abstract idea on a computer that uses a language model in the form of an artificial intelligence model. This is the equivalent of reciting “apply it” with a computer for the abstract idea and is taken as a link to a particular technological environment, that is the use of computers and artificial intelligence (machine learning). The rationale set forth for the 2nd prong of the eligibility test above is also applicable to step 2B in this regard so no further comments are necessary. This is consistent with the PEG found in the MPEP 2106.
Therefore, claims 1, 8, 15, do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claims 2, 9, 16, the receiving of a binary indication of whether reportable criterion is included in the incident data is a further recitation to the same abstract idea of claims 1, 8, 15. A person can receive a binary indication such as a yes or no answer as to whether or not the incident data includes reportable data. This is claiming part of the abstract idea. No further additional elements have been claimed for consideration beyond those recited in the independent claims. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claims 3, 10, 17, identifying whether the incident data includes the reportable criteria is part of the abstract idea as was set forth for claims 1, 8, 15. The training data including first training data as claimed and second training data as claimed are elements that are also considered to be part of the abstract idea. Training data is just data or information per se and is not eligible subject matter. The recitation to training of the classifier is claiming that the classifier is artificial intelligence, whereas the classifier in claims 1, 8, 15 can be a human being due to the claim scope. The training the classifier is an additional element that is interpreted as being a link to the use of artificial intelligence, as was stated for the independent claims. Artificial intelligence (machine learning) by definition employs a trained model that has been trained on training data. Training a classifier is reciting training of a model and is an instruction for one to use a trained model to perform a step of the abstract idea and/or can be construed as a link to a particular technological environment, namely the field of artificial intelligence or machine learning. The recitation to training the classifier based on first and second data training sets is a link to machine learning and does not provide for integration into a practical application or significantly more, for the same reasons already addressed for claims 1, 8, 15, see MPEP 2106.05(f), (h). The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claims 4, 11, 18, the claimed data that is the reportable criteria is part of the abstract idea. The data of the incident such as if the incident has a fix identified, or if the incident is resolved, are elements that fall under the umbrella of the abstract idea. No further additional elements have been claimed for consideration beyond those recited in the independent claims. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
For claims 5-7, 12-14, 19, 20, claiming that the summarization request includes a style criterion is part of the abstract idea, as well as reciting that the style indicates one or more users are technical users, reciting that the style indicates a maximum number of words to be included, reciting anonymizing the incident status report, and excluding social media tags. This is claiming the format or appearance or content for the status report that is being output. This is part of the abstract idea. No further additional elements have been claimed for consideration beyond those recited in the independent claims. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B. Therefore the claims are not considered to be eligible.
Therefore, for the above reasons Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, 8, 9, 15, 16, is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wei et al. (20220366146).
For claims 1, 8, 15, Wei discloses a computerized system (memory, processor) and method for reporting incidents, tracking incidents, and using language models to create status reports for incidents in an automated manner, see paragraph 009 in general.
The claimed obtaining of data associated with an incident is satisfied by Wei disclosing that an incident can be reported about an emergency, see paragraph 029, 032. The receipt of information indicating that an incident such as an emergency has occurred satisfies the claimed data associated with an incident.
The claimed obtaining of the set of reportable criteria related to the incident is satisfied by any of the information that is contained in the incident logs, see paragraph 017. After an incident is first reported, any further activity or events related to the incident or further messages related to the incident that are received, are recorded in what Wei refers to as “incident logs”. The incident logs include more information about an incident that has occurred. The receipt of further message or more information about the incident satisfies the claimed obtaining of the reportable criteria.
The claimed identifying using a classifier of a subset of the reportable criteria is satisfied by the use of a classifier in Wei to analyze the incident logs and incident data contained therein to identify words or phrases that are indicative of the status of the incident, so that they can be provided to users as updates. The analysis of the incident logs by a classifier 202 in Wei is done to identify information regarding the incident and the status of the incident, see paragraphs 017, 024. In paragraph 018 it is disclosed that as an incident progresses, the system automatically extracts and analyzes the incoming incident logs with the classifier so that an updated and current status for the incident can be determined. This satisfies what is claimed.
The claimed transmitting of a summarization request to a language model, that includes the subset of reportable criteria and the incident information, is satisfied by Wei teaching that a natural language generator is used to generate responses, including generating a status report upon request, see paragraphs 020, 038. A user can request a status report or the system can provide an update when the incident logs indicate a change in the status of the incident.
The claimed transmitting of an incident status report is disclosed in paragraph 015, 020, 038. After the summarization request is submitted to the language model in Wei, the output of the language model is a status report for the incident, and that includes the updated status as is taught by Wei. This satisfies the claimed transmitting of an incident status report that is the output from the language model.
For claims 2, 9, 16, the claimed receiving of a binary indication of whether a reportable criterion is included in the incident data is the classifier of Wei determining that an incident log contains new information for the incident that needs to be reported, such as an updated status for the incident. See paragraph 020, 031.
Claim Rejections - 35 USC § 103
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) 3, 4, 10, 11, 17, 18, is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (20220366146).
For claims 3, 10, 17, Wei discloses that the classifier needs to be trained, see paragraph 017, 024, 028. Paragraph 028 teaches that the training data needs to be annotated for training, which is implying to one of ordinary skill in the art the use of supervised machine learning although it is not expressly disclosed. The classifier of Wei is a pre-trained deep learning model that requires training. Not discloses is first training data that includes reportable criteria and second training data that does not include reportable criteria. This is interpreted to be the use of supervised machine learning that uses data training sets that have positive examples and negative examples for the training. The examiner takes official notice of the fact that supervised machine learning uses training data that has been annotated as positive examples and annotated as negative examples for the model to learn from. This is a well-known manner by which machine learning models can be trained and is something that is very well known to those of ordinary skill in the artificial intelligence (machine learning) art. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide Wei with the ability to train the classifier using training data that has examples with reportable criteria and with examples that does not have reportable criteria, so that the classifier can be trained using supervised machine learning. This is a well-known way to train a machine learning model and would yield predictable results of allowing the classifier model of Wei to be trained using supervised machine learning that has a positive data training set and a negative data training set.
For claims 4, 11, 18, the claimed reportable criteria is claiming data relating to various aspects about the incident and that indicates status of the incident such as a cause of the incident (a fire) or a change in severity (an update to the incident). These are satisfied by Wei. The reportable criteria is satisfied by the content of the incident logs in Wei that represents the incident and any updates or changes in information for the incident. Any information that is received about an incident satisfies the claimed reportable criteria. Not disclosed is that the criteria includes if the incident is under investigation, a level of customer impact, whether a fix is identified, a change in severity and whether or not it is resolved. The language of these claims is directed at non-functional descriptive material that does not define more than criteria in general. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the incident logs able to include data such as if the incident is under investigation, a level of customer impact, whether a fix is identified, a change in severity and whether or not it is resolved so that the incident and its status can be known. The claimed aspects to an incident are fully within the purview of one of ordinary skill in the art as far as when an incident occurs, people want to know if it is under investigation or what impact the incident has or whether it has been fixed or not, it is has gotten worse, etc.. What is claimed as far as criteria that can define an incident are things that would have been obvious to one of ordinary skill in the art.
Claim(s) 5-7, 12, 19, 20, is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (20220366146) in view of O’Donncha et al. (21210312122).
For claims 5, 12, 19, not disclosed is that the summarization request includes a style criteria. This element is reciting the aspect of the invention that uses the language model to customize or tailor the output of the language model to a diverse audience.
O’Donncha et al. (21210312122) discloses a system and method for generating documents with a particular style. See paragraph 014. In paragraph 002 it is disclosed that effective communication depends on appropriate style, tone, descriptiveness, concision, vocabulary given the intended reader or audience. Paragraph 081 discloses that a language model can be used to generate output that is based on a target style.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide Wei with the ability to customize the output of the language model using style criteria as set forth by O’Donncha. This would yield the predictable results of allowing for the report to be tailored to certain audiences using a particular style, that would depend on the audience at hand.
For claims 6, 20, the combination above for claims 5, 15, is considered to satisfy the claimed element where the style indicates if one or more users are technical users. This is claiming a particular type of audience member that the style is for. In paragraph 012 of O’Donncha it is disclosed that the style can be tailored for users such as readers of a scientific journal as opposed to elementary school students. This satisfies what is claimed because readers of scientific journal are technical users (the term technical does not define anything to the user itself).
For claims 7, 20, Wei as modified in view of O’Donncha does not teach that the style includes maximum number of words that the report is to be limited to. O’Donncha teaches that concision is a consideration to make when rendering a document for communication with a user. Concision is defined as the act of being concise. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide Wei (as modified with O’Donncha) with style that limits a document communication to a certain number of words so that the report is concise and not too long. Nobody wants to read a 20 page report when the information can be provided in 1 or 2 pages by being concise. Limiting the incident report to a maximum numbers of words would have been obvious in view of a desire to be concise with the content of the report.
Claim(s) 13, 14, is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (20220366146) in view of O’Donncha et al. (21210312122) and further in view of Blumenfeld et al. (20150244681).
For claims 13, 14, not disclosed is that the style indicates that the incident report data should be anonymized or that social media tags are removed.
For claims 13, 14, Blumenfeld teaches a system and method for anonymous incident reporting where an incident report that contains incident data is anonymized, see the abstract and paragraph 005, 006, 009 as examples. This ensures that incident information is kept anonymous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide Wei with the ability to anonymize the incident report as claimed. Anonymizing of data and reports as is taught by Blumenfeld is known in the art and would have been obvious to provide to Wei.
For claim 14, while not disclosed in the cited art, it would have been obvious to one of ordinary skill in the art to remove social media tags when anonymizing the incident report data because social media tags can be used to identify a person, such as a person that is reporting an incident who wants to remain anonymous, or for an update to the incident that is received by the system of Wei and that is to be anonymized. If one is anonymizing the incident report data, then it follows that one of ordinary skill in the art would want to remove social media tags as part of the anonymization of the incident report. This yields the predictable result of ensuring that social media tags are removed to maintain anonymity.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ferraro et al. (20090235084) discloses an anonymous reporting system that is relevant to the claimed invention. Content of notifications about an incident are determined so that alerts can be generated to parties that have permission to the information.
Benoit et al. (20160217669) discloses an incident reporting system that produces anonymized reports. This is relevant to the claims that recite the anonymization of the incident report.
Rauner (20180199179) discloses an emergency messaging system that analyzes messages about incidents to determine content, and that might trigger a Clery event that requires a report of the incident, see paragraph 084.
Puri et al. (20190097909) discloses an incident reporting management system that can generate incident update reports, see paragraph 437. This is relevant to the claimed invention.
Goyal et al. (20240370764, filed 05/03/23), teaches in paragraph 014 that an incident report can be generated using an AI summary service. This is relevant to the part of the claimed invention that is using artificial intelligence (the language model) to generate the incident report.
Ethem Alpaydin, "Supervised Learning," in Introduction to Machine Learning , MIT Press, 2014, pp.21-47 discloses that supervised machine learning can include use of training data that has positive examples and negative examples. This reference is cited in support of the taking of official notice by the examiner with respect to claims 3, 10, 17.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS WILLIAM RUHL whose telephone number is (571)272-6808. The examiner can normally be reached M-F 7am-3:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached at 5712703445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DENNIS W RUHL/Primary Examiner, Art Unit 3626