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 .
Response to Arguments
1. Applicant's arguments have been fully considered as follows:
Claim Rejections under 35 U.S.C. 112(a) – Examiner agrees, rejections have been withdrawn.
Claim Rejections under 35 U.S.C. 112(b) – Examiner agrees, rejections have been withdrawn.
Claim Rejections under 35 U.S.C. 103 – Examiner respectfully disagrees.
Applicant argues that “Kotaru does not teach that the query comprises the response to the query.” Examiner notes that this is a misunderstanding how Examiner is applying the teachings of Kotaru.
First, Examiner notes that Kotaru is utilizing a natural language processor, i.e. it is not merely taking the data as simply raw data, but rather it is able to arrive at an “understanding” of the data. As such, Examiner does not limit what Kotaru “receives” as merely the raw data.
In the case of receiving a “query,” Kotaru necessarily understands that there is a response being desired (and hence the received data comprises a “query” and not a statement). As such, the query can be understood as comprising a problem description and a desired outcome. For example, if the query is something like “how to fix a radio station, when it is not working?” then this is understood as a desired outcome, i.e. a fixed radio station, and a problem description, i.e. a radio station that isn’t working.
The usage of natural language processing merely means that this information can be presented in a natural language form, such as a query, rather than data with descriptions.
Applicant argues that “for a cell site” is not intended use.
First, Applicant’s distinction does not apply the claims. While a container “for holding liquids” presents a direct modifier upon the container, this is different than the claim language. “For a cell site” does not present a direct modifier, rather “a cell site” is an indirect object, i.e. a noun or pronoun that receives the direct object (a problem description and a desired outcome) in a sentence, answering the questions "to whom?" or "for whom?" the action is performed.
Second, Applicant’s amendment to “regarding” is taught by the claims as the query on operator metric data is regarding at least one cell site.
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.
3. Claims 1-4, 6-12, 14, 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kotaru (US 20240419705 A1) in view of Gailloux et al. (US 11070672 B1).
Claim 1 Kotaru teaches one or more computing systems of a carrier network, comprising:
memory (FIG. 6, system memory 604) storing computer program instructions for an artificial intelligence (AI) / machine learning (ML) observability platform; (When reading the preamble in the context of the entire claim, the recitation “an artificial intelligence (AI) / machine learning (ML) observability platform” is not limiting because the body of the claim describes a complete invention and the language recited solely in the preamble does not provide any distinct definition of any of the claimed invention’s limitations. Thus, the preamble of the claim is not considered a limitation and is of no significance to claim construction. See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See MPEP § 2111.02.) and at least one processor (FIG. 6, processing unit 602) configured to execute the computer program instructions, wherein the computer program instructions are configured to cause the at least one processor to:
receive a problem description and a desired outcome for a cell site (FIG. 3, step 302, ¶0048, receiving a natural language query, wherein a query comprises a problem description, i.e. the query itself, and a desired outcome, i.e. the response to the query, herein the query on operator metric data is regarding cell sites) and determine an intent from the problem description and the desired outcome (FIG. 3, step 304, ¶0049, determine an intent via extracting metric context, i.e. intent, using a natural language query) using an pre-existing intent determination AI/ML model, (FIG. 1, ¶0024-¶0025, wherein the natural language generate module uses a machine learning model to generate outputs)
obtain data pertaining to one or more radios of the cell site (¶0049-¶0050, obtaining metric context pertaining to the query, i.e. to the cell site) and send the data to a pre-existing vectorizing AI/ML model (FIG. 3, step 306, ¶0050, generating a prompt for a machine learning model) trained to convert the data to vector embeddings, (Examiner notes that this is intended use and outside the claim scope, and does not have patentable weight)
receive the vector embeddings from the vectorizing AI/ML model, (FIG. 3, step 308, ¶0062, receiving an output from the machine learning model) comparing the vector embeddings to a vector database, (¶0033, comparing the metric data to a metric database) and providing an answer. (FIG. 3, step 316, ¶0055, generating a dashboard comprising the answer or recommendation.
However, Kotaru does not explicitly teach determining one or more radios that are a cause of the problem using one or more pre-existing radio problem isolation AI/ML models, and
recommending resting the one or more radios or, in response to the determining of one or more radios that are a cause of the problem, rest the one or more radios.
From a related technology, Gailloux determining one or more radios that are a cause of the problem using one or more pre-existing radio problem isolation AI/ML models, (Col. 3, Lines 27-29, determining that unused radios caused reported rapid discharge problem) and recommending resting the one or more radios (Col. 3, Lines 27-29, recommending turning off one or more radios to solve the problem) or, in response to the determining of one or more radios that are a cause of the problem, rest the one or more radios.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kotaru to incorporate the recommendation determination techniques implemented in Gailloux in order to provide effective solutions for the data intelligence model query database to effective address user problem queries.
Claim 2 Kotaru in view of Gailloux teaches Claim 1, and further teaches wherein the data pertaining to the one or more radios of the cell site comprises cell site coverage ranges, neighborhood relationship statistics, location data, antenna entity data, average cell availability data, uplink and downlink usage statistics, or any combination thereof. (¶0018, Kotaru wherein the metrics comprise uplink throughput)
Claim 3 Kotaru in view of Gailloux teaches Claim 1, and further teaches collect data over a period of time from radios of a plurality of cell sites; (¶0049-¶0050, obtaining metric context pertaining to the query, i.e. to the cell sites)
train the vectorizing AI/ML model and the one or more radio problem isolation AI/ML models; (See 11(b) rejection, Kotaru, FIG. 1, ¶0024-¶0025, wherein using the model is training the model, Gailloux, Col. 3, Lines 27-29, wherein using the model is training the model)
deploy the vectorizing AI/ML model and the one or more radio problem isolation AI/ML models; (See 112(a) rejection, Examiner interprets using as “deploying” the model; Kotaru, FIG. 1, ¶0024, using the model, Gailloux, Col. 3, Lines 27-29, using the model) and
create the vector database using the trained vectorizing AI/ML model. (Kotaru, ¶0033, constructing the vector database using the model)
Claim 4 Kotaru in view of Gailloux teaches Claim 1, and further teaches continuously monitor radios from a plurality of cell sites and update the vector database using the trained vectorizing AI/ML model. (Kotaru, FIG. 2, ¶0044, ¶0044, monitoring metrics and updating the specific databases using the foundational model)
Claim 6 Kotaru in view of Gailloux teaches Claim 1, and further teaches measure performance data of the cell site and the radios of the cell site (Kotaru, ¶0049-¶0050, obtaining metric context pertaining to the query, i.e. to the cell site, wherein the metrics are measurements of performance of the cell site) during the resting of the one or more radios that are the most likely cause of the problem; (See 112(b) rejection, the claim is unclear the radios are never rested, rather only a recommendation has been made; Examiner further notes that Kotaru would teach measuring performance data regardless of radios resting or not) and
send the measured performance data to a retraining database (Kotaru, ¶0044, outputting the metrics to the database, wherein the database) for retraining the vectorizing AI/ML model and the one or more radio problem isolation AI/ML models. (Examiner notes that this is an intended use statement)
Claim 7 Kotaru in view of Gailloux teaches Claim 6, and further teaches retrain the vectorizing AI/ML model and the one or more radio problem isolation AI/ML models using the measured performance data; (See 11(b) rejection, Kotaru, FIG. 1, ¶0024-¶0025, wherein using the model is retraining the model, Gailloux, Col. 3, Lines 27-29, wherein using the model is retraining the model) and
deploy the retrained vectorizing AI/ML model and the retrained one or more radio problem isolation AI/ML models for use by the AI/ML observability platform. (See 112(a) rejection, Examiner interprets using as “deploying” the model; Kotaru, FIG. 1, ¶0024, using the model, Gailloux, Col. 3, Lines 27-29, using the model)
Claim 8 Kotaru in view of Gailloux teaches Claim 1, and further teaches wherein the one or more radio problem isolation AI/ML models are trained to recommend a type of rest for each of the one or more radios that are the most likely cause of the problem, (Gailloux, Col. 3, Lines 27-30, wherein turning off a radio is a type of rest for a radio, i.e. a hard rest) and the resting of the one or more radios comprises performing a soft rest, performing a hard rest, or both. (Gailloux, Col. 3, Lines 27-30, comprising a hard rest)
Claim 9 Kotaru teaches one or more non-transitory computer-readable media storing one or more computer programs for an artificial intelligence (AI) / machine learning (ML) observability platform, the one or more computer programs configured to cause at least one processor to:
receive a problem description and a desired outcome (FIG. 3, step 302, ¶0048, receiving a natural language query, wherein a query comprises a problem description, i.e. the query itself, and a desired outcome, i.e. the response to the query) for a cell site (Examiner notes that this is intended use and does not have patentable weight) and determine an intent from the problem description and the desired outcome (FIG. 3, step 304, ¶0049, determine an intent via extracting metric context, i.e. intent, using a natural language query) using an intent determination AI/ML model, (FIG. 1, ¶0024-¶0025, wherein the natural language generate module uses a machine learning model to generate outputs)
obtain data pertaining to one or more radios of the cell site (¶0049-¶0050, obtaining metric context pertaining to the query, i.e. to the cell site) and send the data to vectorizing AI/ML model (FIG. 3, step 306, ¶0050, generating a prompt for a machine learning model) trained to convert the data to vector embeddings, (Examiner notes that this is outside the scope of the claim and is an intended use statement, and does not have patentable weight)
receive the vector embeddings from the vectorizing AI/ML model, (FIG. 3, step 308, ¶0062, receiving an output from the machine learning model) comparing the vector embeddings to a vector database, (¶0033, comparing the metric data to a metric database) and providing an answer. (FIG. 3, step 316, ¶0055, generating a dashboard comprising the answer or recommendation.
However, Kotaru does not explicitly teach determining one or more radios that are a most likely cause of the problem using one or more radio problem isolation AI/ML models, and recommending resting the one or more radios or automatically resting the one or more radios, wherein the one or more radio problem isolation AI/ML models are trained to recommend a type of rest for each of the one or more radios that are the most likely cause of the problem, and the resting of the one or more radios comprises performing a soft rest, performing a hard rest, or both.
From a related technology, Gailloux determining one or more radios that are a most likely cause of the problem using one or more radio problem isolation AI/ML models, (Col. 3, Lines 27-29, determining that unused radios caused reported rapid discharge problem) and recommending resting the one or more radios or automatically rest the one or more radios, (Col. 3, Lines 27-29, recommending turning off one or more radios to solve the problem) wherein the one or more radio problem isolation AI/ML models are trained to recommend a type of rest for each of the one or more radios that are the most likely cause of the problem, (Gailloux, Col. 3, Lines 27-30, wherein turning off a radio is a type of rest for a radio, i.e. a hard rest) and the resting of the one or more radios comprises performing a soft rest, performing a hard rest, or both. ((Gailloux, Col. 3, Lines 27-30, comprising a hard rest)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kotaru to incorporate the recommendation determination techniques implemented in Gailloux in order to provide effective solutions for the data intelligence model query database to effective address user problem queries.
Claim 10 is taught by Kotaru in view of Gailloux as described for Claim 2.
Claim 11 is taught by Kotaru in view of Gailloux as described for Claim 3.
Claim 12 is taught by Kotaru in view of Gailloux as described for Claim 4.
Claim 14 is taught by Kotaru in view of Gailloux as described for Claim 6.
Claim 16 Kotaru teaches a computer-implemented method for performing artificial intelligence (AI)-driven radio reset selection, comprising:
receive a problem description and a desired outcome (FIG. 3, step 302, ¶0048, receiving a natural language query, wherein a query comprises a problem description, i.e. the query itself, and a desired outcome, i.e. the response to the query) for a cell site (Examiner notes that this is intended use and does not have patentable weight) and determine an intent from the problem description and the desired outcome (FIG. 3, step 304, ¶0049, determine an intent via extracting metric context, i.e. intent, using a natural language query) using an intent determination AI/ML model, (FIG. 1, ¶0024-¶0025, wherein the natural language generate module uses a machine learning model to generate outputs)
obtain data pertaining to one or more radios of the cell site (¶0049-¶0050, obtaining metric context pertaining to the query, i.e. to the cell site) and send the data to vectorizing AI/ML model (FIG. 3, step 306, ¶0050, generating a prompt for a machine learning model) trained to convert the data to vector embeddings, (Examiner notes that this is outside the scope of the claim and is an intended use statement, and does not have patentable weight)
receive the vector embeddings from the vectorizing AI/ML model, (FIG. 3, step 308, ¶0062, receiving an output from the machine learning model) comparing the vector embeddings to a vector database, (¶0033, comparing the metric data to a metric database) and providing an answer. (FIG. 3, step 316, ¶0055, generating a dashboard comprising the answer or recommendation.
However, Kotaru does not explicitly teach determining one or more radios that are a most likely cause of the problem using one or more radio problem isolation AI/ML models, by the computing system; and
recommending resting the one or more radios or automatically resting the one or more radios, by the computing system, wherein the data pertaining to the one or more radios of the cell site comprises cell site coverage ranges, neighborhood relationship statistics, location data, antenna entity data, average cell availability data, uplink and downlink usage statistics, or any combination thereof.
From a related technology, Gailloux determining one or more radios that are a most likely cause of the problem using one or more radio problem isolation AI/ML models, (Col. 3, Lines 27-29, determining that unused radios caused reported rapid discharge problem) and recommending resting the one or more radios or automatically rest the one or more radios, (Col. 3, Lines 27-29, recommending turning off one or more radios to solve the problem) wherein the data pertaining to the one or more radios of the cell site comprises cell site coverage ranges, neighborhood relationship statistics, location data, antenna entity data, average cell availability data, uplink and downlink usage statistics, or any combination thereof. (¶0018, Kotaru wherein the metrics comprise uplink throughput)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kotaru to incorporate the recommendation determination techniques implemented in Gailloux in order to provide effective solutions f
Claim 18 is taught by Kotaru in view of Gailloux as described for Claim 4
.
4. Claims 5, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kotaru (US 20240419705 A1) in view of Gailloux et al. (US 11070672 B1) and in further view of Yajnanarayana et al. (US 20240023077 A1).
Claim 5 Kotaru in view of Gailloux teaches Claim 1, and further teaches displaying an answer. (Kotaru, FIG. 3, step 316, generating and displaying an answer)
However, Kotaru in view of Gallioux does not explicitly teach generating a knowledge graph for a radio of the one or more radios that are the most likely cause of the problem using the vector database; and
wherein the knowledge graph is the answer.
From a related technology, Yajnanarayana teaches generate a knowledge graph for a radio of the one or more radios that are the most likely cause of the problem (FIG. 4, knowledge graph 400, ¶0052, wherein the knowledge graph is for one or more radios that are likely to cause a problem, for example cause interference or noise)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kotaru in view of Gailloux to incorporate knowledge graph analytical techniques for resolving data problems as taught by Yajnanarayana in order to more effectively analyze and present results to data problems.
Claim 13 is taught by Kotaru in view of Gailloux and Yajnanarayana as described for Claim 5.
Claim 19 is taught by Kotaru in view of Gailloux and Yajnanarayana as described for Claim 5
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER PALACA CADORNA whose telephone number is (571)270-0584. The examiner can normally be reached M-F 10:00-7:00.
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, John Follansbee can be reached at (571) 272-3964. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER P CADORNA/Examiner, Art Unit 2444
/JOHN A FOLLANSBEE/Supervisory Patent Examiner, Art Unit 2444
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