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 .
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 under Step 1 are directed towards a system (Claims 1-10) and a method (Claims 11-20) for troubleshooting issues. The independent claims 1 and 11 recite “receive source data from a plurality of data sources; extract a plurality of keywords from the source data; store the plurality of keywords in a database; receive a natural-language user issue input from a chatbot; determine, based on a comparison of the received user issue input to the stored keywords, whether the received user input matches the keywords in the database, wherein a comparison result is inputted into a trained model; and input the user issue input into a trained model, wherein the trained model is based on a previous pair of user issue inputs mapped to resolutions and the comparison result; in response to the determining, transmit the resolution from the trained model to the chatbot”.
The limitation of “receive source data...” “extract a plurality of keywords …”, “receive a natural-language user issue input … “determine, based on a comparison …”, “input the user issue input into a trained model…” and “transmit the resolution …” as drafted covers a human organizing of activities. More specifically, a human seeing receiving texts with user issues and applying NLP processes and formulas to troubleshoot/resolve user’s issue using past resolutions. All these activities can be performed by writing down on piece of a paper using a pen or using a generic machine and the claim language therefore appears to be merely an abstract idea, a mental process that is able to be performed by a person in their mind. While in claims 1 and 11, a trained model is used, the model is described at a high level of generality without any particular details regarding how the model “trained” As such, a human equipped with pen and paper or a generic machine could follow a similar “model” to arrive at the claimed generated and identified data.
This judicial exception is not integrated into a practical application. In particular, claims 1 and 11 recite additional elements – “memory” and “processor”. The additional element, “memory” and “processor”, are well known and generic components used conventionally in most of the generic computer devices. Also, it is known that CRM or computer-implementation of an abstract idea is not a factor that weighs in favor of patentability under subject matter eligibility. In addition, “memory” and “processor”, as suggested are generic elements and account to no additional limits that may result in subject matter eligibility. According, these 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 claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer is noted as a general computer as noted. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, “receive source data from a plurality of data sources; extract a plurality of keywords from the source data; store the plurality of keywords in a database; receive a natural-language user issue input from a chatbot; determine, based on a comparison of the received user issue input to the stored keywords, whether the received user input matches the keywords in the database, wherein a comparison result is inputted into a trained model; and input the user issue input into a trained model, wherein the trained model is based on a previous pair of user issue inputs mapped to resolutions and the comparison result; in response to the determining, transmit the resolution from the trained model to the chatbot” are directed towards insignificant extra solution activity such as collecting data and then using results/data, as supported by the MPEP, “Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g))”. Furthermore, “receive source data from a plurality of data sources; extract a plurality of keywords from the source data; store the plurality of keywords in a database; receive a natural-language user issue input from a chatbot; determine, based on a comparison of the received user issue input to the stored keywords, whether the received user input matches the keywords in the database, wherein a comparison result is inputted into a trained model; and input the user issue input into a trained model, wherein the trained model is based on a previous pair of user issue inputs mapped to resolutions and the comparison result; in response to the determining, transmit the resolution from the trained model to the chatbot” amounts to merely applying the mental process using a computer, which are not enough to qualify as significantly more under the MPEP, “Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f))”. Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claims 2 and 12 are dependent on independent claims 1 and 11 and include all the limitations of claims 1 and 11. Claims 2 and 12 recite “wherein the data sources comprise one or more of mail data, ticket data, project management data, incident report data” (mental process – observation, evaluation, judgment, opinion). The claim language provides only further specifying what the data used in the underlying mental process. Claims 2 and 12 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claims 3 and 13 are dependent on independent claims 1 and 11 and include all the limitations of claims 1 and 11. Claims 3 and 13 recite “wherein the data sources are synced with one or more cloud systems or software communication systems” (mental process – observation, evaluation, judgment, opinion). The claim language provides only further specifying what the data used in the underlying mental process. Claims 3 and 13 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claims 4 and 14 are dependent on independent claims 1 and 11 and include all the limitations of claims 1 and 11. Claims 4 and 14 recite “wherein the data source comprises images; the processor is further configured to extract image source data with a cognitive vision tool; and the processor is further configured to store the image source data in the database” (mental process – observation, evaluation, judgment, opinion). The claim language provides only further specifying what the data used in the underlying mental process. Claims 4 and 14 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claims 5 and 15 are dependent on independent claims 1 and 11 and include all the limitations of claims 1 and 11. Claims 5 and 15 recite “wherein the processor is further configured to convert the source data to JSON format” (mental process – observation, evaluation, judgment, opinion). The claim language provides only further specifying what the data used in the underlying mental process. Claims 5 and 15 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claims 6 and 16 are dependent on independent claims 1 and 11 and include all the limitations of claims 1 and 11. Claims 6 and 16 recite “wherein the previous pair of user issue inputs mapped to resolutions is sorted according to at least one of a sorting metric, user feedback, or manual sorting” (mental process – observation, evaluation, judgment, opinion). The claim language provides only further specifying what the data used in the underlying mental process. Claims 6 and 16 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claims 7 and 17 are dependent on independent claims 1 and 11 and include all the limitations of claims 1 and 11. Claims 7 and 17 recite “wherein the trained model is configured to map at least one of a project name or the keywords to a category” (mental process – observation, evaluation, judgment, opinion). The claim language provides only further specifying what the data used in the underlying mental process. While in claims 7 and 17, a trained model is used, the model is described at a high level of generality without any particular details regarding how the model “trained” As such, a human equipped with pen and paper or a generic machine could follow a similar “model” to arrive at the claimed generated and identified data. Claims 7 and 17 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claims 8 and 18 are dependent on independent claims 1 and 11 and include all the limitations of claims 1 and 11. Claims 8 and 18 recite “wherein: the determining comprises determining that the received user input does not match at least a portion of the stored keywords and the resolution; and in response to the determining, transmitting the resolution with no resolution” (mental process – observation, evaluation, judgment, opinion). The claim language provides only further specifying what the data used in the underlying mental process. Claims 8 and 18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claims 9 and 19 are dependent on independent claims 1 and 11 and include all the limitations of claims 1 and 11. Claims 9 and 19 recite “wherein: the determining comprises determining that the received user input does match at least a portion of the stored keywords and the resolution; and in response to the determining, transmitting the at least one resolution” (mental process – observation, evaluation, judgment, opinion). The claim language provides only further specifying what the data used in the underlying mental process. Claims 9 and 19 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claims 10 and 20 are dependent on independent claims 1 and 11 and include all the limitations of claims 1 and 11. Claims 10 and 20 recite “wherein the processor is further configured to remove undesired source data” (mental process – observation, evaluation, judgment, opinion). The claim language provides only further specifying what the data used in the underlying mental process. Claims 10 and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
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.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4, 7, 9-11, 14, 17, 19-20 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by U.S. Patent Application Publication No. 20230259821 to Travalini et al. (“Travalini”).
As to claims 1 and 11, Travalini discloses a system and a method for troubleshooting issues based on historical resolution data [Abstract, Figs. 1-10, pages 1-23], the system comprising: memory [paragraph 0144]; and at least one processor [paragraph 0144], the memory containing software code configured to cause the processor to [paragraph 0144]: receive source data from a plurality of data sources [paragraph 0065: “a data store (e.g., graph database) may store the relationships between collected training data and current data and other inputs”, paragraph 0084: “the input nodes 110 a-n may comprise logical inputs of different data sources [i.er. receive source data from a plurality of data sources], such as one or more data servers. The processing nodes 120 a-n may comprise parallel processes executing on multiple servers in a data center. And, the output nodes 140 a-n may be the logical outputs that ultimately are stored in results data stores”];
extract a plurality of keywords from the source data [paragraph 0058: "; The one or more NLP algorithms may analyze the text to identify one or more keywords [i.e. extract a plurality of keywords from the source data] and/or phrases. The one or more NLP algorithms may determine whether the inputs match existing classifications and/or identifiers”];
store the plurality of keywords in a database [paragraph 0114];
receive a natural-language user issue input from a chatbot [paragraphs 0042, Fig. 2A shows an interaction between the chatbot and the user device via text message [i.e. receive a natural-language user issue input from a chatbot];
determine, based on a comparison of the received user issue input to the stored keywords, whether the received user input matches the keywords in the database, wherein a comparison result is inputted into a trained model [paragraph 0058]; and
input the user issue input into the trained model, wherein the trained model is based on a previous pair of user issue inputs mapped to resolutions and the comparison result [paragraph 0058];
in response to the determining, transmit the resolution from the trained model to the chatbot [Fig. 2A, 5A, paragraph 0043: “a bathroom location may be inferred by the chatbot (e.g., the machine learning underlying the chatbot) if a user indicates a problem with a toilet, a shower, a bathtub, etc. Similarly, the chatbot may be able to infer an item based on a component identified in the conversation. For example, a resident may identify a problem as "a flapper not seating correctly" Like the example above, the chatbot (e.g., the machine learning supporting the chatbot) may infer that the problem is with a toilet based on the user identifying the flapper. Based on the information received from the user and the information that the chatbot is able to infer, the chatbot may determine and select an appropriate response “ (i.e. in response to the determining), paragraph 0046: “If the user indicates that he or she does want to try to resolve the issue, the chatbot may provide one or more instructions (e.g., steps) for the user to perform in an attempt to resolve the issue” (i.e. transmit the resolution from the trained model to the chatbot)].
As to claims 4 and 14, Travalini discloses wherein the data source comprises images [paragraph 0051: “the interaction shows a user uploading (e.g., sending) images of an item to the chatbot’; the processor is further configured to extract image source data with a cognitive vision tool [paragraph 0051: “Upon receiving the images, the chatbot (e.g., the machine learning algorithms supporting the chatbot) may perform image analysis to identify information and/or details about the item. The information and/or details make include a make and model of the device and/or a serial number of the device. In some examples, the information and/or details may include an image or video illustrating the problem the user is having”]; and the processor is further configured to store the image source data in the database [paragraph 0065: “a data store (e.g., graph database) may store the relationships between collected training data and current data and other inputs” (i.e. store the image source data in the database)].
As to claims 7 and 17, Travalini discloses wherein the trained model is configured to map at least one of a project name or the keywords to a category [paragraph 0129: “ tuning parameters may comprise various descriptions of a plurality of problems as well as categorizations that correspond to each problem. For example, at least one LLM may be configured to recognize “ac,” “aircon,” “air conditioning,” “HVAC,” “window unit,” or “temp unit” as alternative indications of an air conditioning system” (i.e. trained model is configured to map keywords to a category)].
As to claims 9 and 19, Travalini discloses the determining comprises determining that the received user input does match at least a portion of the stored keywords and the resolution [paragraph 0059: “one or more NLP algorithms may be configured to identify an item, a symptom, a location, and/or a component. In some instances, the one or more NLP algorithms may determine whether a threshold amount of information was received. Additionally or alternatively, the one or more NLP algorithms may determine whether a prime symptom has been identified” (i.e. received user input does match at least a portion of the stored keywords and the resolution)]; and in response to the determining, transmitting the at least one resolution [paragraph 0046: “If the user indicates that he or she does want to try to resolve the issue, the chatbot may provide one or more instructions (e.g., steps) for the user to perform in an attempt to resolve the issue].
As to claims 10 and 20, Travalini discloses wherein the processor is further configured to remove undesired source data [paragraph 0087: “the artificial neural network 100 may be configured to detect faces in photographs. The input nodes 110a-n may be provided with a digital copy of a photograph. The first set of processing nodes 120a-n may be each configured to perform specific steps to remove non-facial content, such as large contiguous sections of the color red” (i.e. remove undesired source data)].
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 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 of this title, 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.
Claims 2, 8, 12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20230259821 to Travalini et al. (“Travalini”) in view of U.S. Patent Application Publication No. 20170262449 to Venkataraman et al. (“Venkataraman”).
As to claims 2 and 12, Travalini discloses the system of claim 1 and the method of claim 11 [See rejection of claims 1 and 11].
Travalini does not expressly disclose wherein the data sources comprise one or more of mail data, ticket data, project management data, incident report data.
In the same or similar field of invention, Venkataraman discloses wherein the data sources comprise one or more of mail data, ticket data, project management data, incident report data [Venkataraman paragraphs 0017, 0020, 0032: “knowledge database 204 includes response candidates (e.g., incident tickets, ticket description, resolution, etc.)” (i.e. data source comprises ticket data)].
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Travalini to have feature of wherein the data sources comprise one or more of mail data, ticket data, project management data, incident report data as taught by Venkataraman. The suggestion/motivation would have been to provide a method for generating an optimized result set based on vector based relative importance measure [Venkataraman Abstract].
As to claims 8 and 18, Venkataraman discloses wherein: the determining comprises determining that the received user input does not match at least a portion of the stored keywords and the resolution [paragraphs 0005, 0037-0040, 0050]; and in response to the determining, transmitting the resolution with no resolution [paragraphs 0004: “take a decision not to respond if none of the responses in the database are relevant”, paragraphs 0005, 0037-0040, 0050: “the techniques may provide no responses if none of the responses are relevant”]. In addition, the same motivation is used as the rejection of claims 2 and 12.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20230259821 to Travalini et al. (“Travalini”) in view of U.S. Patent Application Publication No. 20190205837 to Tuli et al. (“Tuli”).
As to claims 3 and 13, Travalini discloses the system of claim 1 and the method of claim 11 [See rejection of claims 1 and 11].
Travalini does not expressly disclose wherein the data sources are synced with one or more cloud systems or software communication systems.
In the same or similar field of invention, Tuli discloses wherein the data sources are synced with one or more cloud systems or software communication systems [Tuli Fig. 3, paragraphs 0045, 0054].
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Travalini to have feature of wherein the data sources are synced with one or more cloud systems or software communication systems as taught by Tuli. The suggestion/motivation would have been to provide a bot framework for autonomous corporate data sources [Tuli paragraph 0001].
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20230259821 to Travalini et al. (“Travalini”) in view of U.S. Patent Application Publication No. 20230169328 to Haptonstahl (“Haptonstahl”).
As to claims 5 and 15, Travalini discloses the system of claim 1 and the method of claim 11 [See rejection of claims 1 and 11].
Travalini does not expressly disclose wherein the processor is further configured to convert the source data to JSON format.
In the same or similar field of invention, Haptonstahl discloses wherein the processor is further configured to convert the source data to JSON format [Haptonstahl paragraphs 0005, 0021].
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Travalini to have feature of converting the source data to JSON format as taught by Haptonstahl . The suggestion/motivation would have been to improve the quality, efficiency, speed, privacy and scalability of processing big data so that data originated from various data sources may be annotated with a common data infrastructure [Haptonstahl paragraph 0003].
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20230259821 to Travalini et al. (“Travalini”) in view of U.S. Patent Application Publication No. 20190102078 to Bhatt et al. (“Bhatt”).
As to claims 6 and 16, Travalini discloses the system of claim 1 and the method of claim 11 [See rejection of claims 1 and 11].
Travalini does not expressly disclose wherein the previous pair of user issue inputs mapped to resolutions is sorted according to at least one of a sorting metric, user feedback, or manual sorting.
In the same or similar field of invention, Bhatt discloses wherein the previous pair of user issue inputs mapped to resolutions is sorted according to at least one of a sorting metric, user feedback, or manual sorting [Bhatt paragraphs 0132: “The summarized information may be generated for conversations associated with any channel and any locale or any combination of channels and locales. The summarized information may also be generated for conversations occurred within a specific time period, such as the last 90 days. The conversations may be filtered based on, for example, the intent, the status (e.g., completed or incomplete), and the final state of the conversation, and may be sorted according to a user selected order”, also paragraphs 0133, 0146].
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Travalini to have feature of wherein the previous pair of user issue inputs mapped to resolutions is sorted according to at least one of a sorting metric, user feedback, or manual sorting as taught by Bhatt. The suggestion/motivation would have been to provide techniques for analyzing and improving a bot system, and more particularly to an analytic system integrated with a bot system for monitoring, analyzing, visualizing, diagnosing, and improving the performance of the bot system [Bhatt paragraph 0003].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTIM G SHAH whose telephone number is (571)270-5214. The examiner can normally be reached Mon-Fri 7:30am-4pm.
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/ANTIM G SHAH/Primary Examiner, Art Unit 2693