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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Specification
The use of the following trademarks have been noted in this application:
Amazon, Microsoft, Google - ¶0043
Intel - ¶0078
ChatGPT - ¶0141, ¶0151
Wherever the trademark appears, each letter of the trademark should be capitalized, or the proper trademark indication should be included. Each instance of the trademark should be accompanied by the generic terminology.
Although the use of trademarks is permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as trademarks.
Claim Objections
Claims 1 and 13 are objected to because of the following informalities:
Claim 1 should apparently recite: “each of the data segments of the number of data segments”. Similarly for claim 13.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “each of the data segments”. However, “each of the data segments” has been previously introduced. It is unclear whether the “each of the data segments” is referring to the previously introduced “each of the data segments” or is introducing new data segments. This antecedent basis ambiguity renders the scope of the claim indefinite. Similarly for claim 13.
Claim 1 recites “a plurality of data segments”. However, “a number of data segments” has been previously introduced. It is unclear whether the “plurality” is referencing the “a number” or is a new set. This antecedent basis ambiguity renders the scope of the claim indefinite. Similarly for claim 13.
Claim 1 recites “one or more of the data segments”. However, “a number of data segments” and “a plurality of data segments” has been previously introduced. It is unclear whether the “one or more” is referencing the “a number” or “a plurality” or is a new set. This antecedent basis ambiguity renders the scope of the claim indefinite. Similarly for claim 13.
Claim 1 recites “indication of sentiment”. However, an “indication of the overall sentiment” has been previously introduced. It is unclear whether the “indication of sentiment” is referencing the “indication of the overall sentiment” or is a new sentiment. This antecedent basis ambiguity renders the scope of the claim indefinite. Similarly for claim 13.
Claim 1 recites “the data segments having an indication of sentiment corresponding to the indication of the overall sentiment”. However, “data segments having an indication of sentiment corresponding to the indication of the overall sentiment” has not been previously introduced. This antecedent basis ambiguity renders the scope of the claim indefinite. Similarly for claim 13.
Claim 2 recites “obtaining:… an indication of a sentiment associated with the plurality of data segments represented by the text data”. However, claim 1 previously introduces “retrieve one or more of the data segments having an indication of sentiment corresponding to the indication of the overall sentiment.” It is unclear whether the obtained indication of a sentiment is the same as the retrieved indication of a sentiment previously introduced, or corresponds to the indication of the overall sentiment, or is introducing a new sentiment. This antecedent basis ambiguity renders the scope of the claim indefinite. Similarly for claim 14.
Claim 3 recites “wherein the data segments”. However, “a number of data segments” and “a plurality of data segments” has been previously introduced. It is unclear which of this, if either, is referenced. This antecedent basis ambiguity renders the scope of the claim indefinite. Similarly for claim 15.
Claim 6 recites “the data segments having an indication of sentiment contrary to the indication of the overall sentiment”. However, such segments have not been previously introduced. Additionally, “a number of data segments” and “a plurality of data segments” have been previously introduced. It is unclear which of these, if either, is referenced. This antecedent basis ambiguity renders the scope of the claim indefinite. Similarly for claim 18.
Claim 12 recites “is as summary of an overall sentiment”. However, “an indication of overall sentiment” has been previously introduced. It is unclear whether the overall sentiment summarized is the same as the overall sentiment indicated. This antecedent basis ambiguity renders the scope of the claim indefinite.
Dependent claims incorporate all of the limitations of their respective independent or intervening claim(s) and are rejected on the same basis.
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. Claim 1 recite(s):
1. A computer system comprising: a processor; a communications module coupled to the processor; and a memory coupled to the processor, the memory storing instructions that, when executed, configure the processor to: perform segmentation on text data to generate a number of data segments; provide each of the data segments to a machine learning system; obtain, as output of the machine learning system, an indication of a sentiment associated with each of the data segments; obtain an indication of overall sentiment for a plurality of data segments represented by the text data; and provide the indication of the overall sentiment to a device, together with a selectable option to retrieve one or more of the data segments having an indication of sentiment corresponding to the indication of the overall sentiment.
The limitations above as drafted, are a process that, under its broadest reasonable interpretation, covers a mental process, specifically, obtaining and providing an indication of a sentiment for text data segments, but for the recitation of generic computer components. That is, other than reciting “[a] computer system comprising: a processor; a communications module coupled to the processor; and a memory coupled to the processor, the memory storing instructions that, when executed, configure the processor to” and “provide each of the data segments to a machine learning system; obtain, as output of the machine learning system” and “a selectable option to retrieve one or more of the data segments”, nothing in the claim element precludes the limitations from being performed in the human mind. If the claim limitation, under its broadest reasonable interpretation, covers a process which can be practically performed in the human mind, then it falls within the “Mental Process” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim recites the additional elements “[a] computer system comprising: a processor; a communications module coupled to the processor; and a memory coupled to the processor, the memory storing instructions that, when executed, configure the processor to” and “provide each of the data segments to a machine learning system; obtain, as output of the machine learning system” and “a selectable option to retrieve one or more of the data segments”. The computer system, machine learning system, and selectable option do not specify how the sentiment is determined beyond a general computer, selection, and machine learning system. The computer system and machine learning system therefore do not appear to integrate the abstract idea into a practical application and instead appears to merely use a computer as a tool to perform the abstract idea. Accordingly, 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 is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. “[a] computer system comprising: a processor; a communications module coupled to the processor; and a memory coupled to the processor, the memory storing instructions that, when executed, configure the processor to” and “provide each of the data segments to a machine learning system; obtain, as output of the machine learning system” and “a selectable option to retrieve one or more of the data segments” are well-known generic computer technique, and are shown in for example in Neervannan et al. (US Patent Number 11,205,044), Trzyna (US Patent Application Publication 2024/0338860), and Zhu et al. (US Patent Application Publication 2023/0056772) . As discussed above, the steps do not appear to impose any meaningful limits on practicing the abstract idea, and amount to mere instructions to apply the exception using well known computer techniques. The claim is not patent eligible.
Similarly, claim 2 recites the machine learning implementation, which also appears to generally link the abstract idea to a particular technological environment. Claim 2 is not patent eligible. Similarly for claim 14.
Similarly, claim 3 recites additional limitations which can be a mental process. Claim 3 is not patent eligible. Similarly for claim 15.
Similarly, claim 4 recites additional limitations which can be a mental process and/or linked to a general computing environment such as receiving a selection and providing the selected information. Claim 4 is not patent eligible. Similarly for claim 16.
Similarly, claim 5 recites additional limitations which can be a mental process and linked to a general computing environment such as receiving a filter selection and providing the selected information. Claim 5 is not patent eligible. Similarly for claim 17.
Similarly, claim 6 recites additional limitations which can be a mental process and linked to a general computing environment such as receiving a filter selection and providing the selected information. Claim 6 is not patent eligible. Similarly for claim 18.
Similarly, claim 7 recites additional limitations which can be a mental process. Claim 7 is not patent eligible. Similarly for claim 19.
Similarly, claim 8 recites additional limitations which can be a mental process. Claim 8 is not patent eligible. Similarly for claim 20.
Similarly, claim 9-12 recites additional limitations which can be mental processes. Claims 9-12 are not patent eligible.
Prior Art
Listed herein below are the prior art references relied upon in this Office Action:
Neervannan et al. (US Patent Number 11,205,044), referred to as Neervannan herein.
Trzyna (US Patent Application Publication 2024/0338860), referred to as Trzyna herein.
Examiner’s Note
Strikethrough notation in the pending claims has been added by the Examiner.
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) 1-11, and 13-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Neervannan.
Regarding claim 1, Neervannan discloses a computer system comprising: a processor; a communications module coupled to the processor; and a memory coupled to the processor, the memory storing instructions that, when executed, configure the processor to (Neervannan, 3:14-4:3 – computers with processors executing instructions stored in hardware memory):
perform segmentation on text data to generate a number of data segments (Neervannan, 6:17-7:10, 9:20-46 – splitting the text into sentence segments that are statements);
provide each of the data segments to a machine learning system; obtain, as output of the machine learning system, an indication of a sentiment associated with each of the data segments (Nervannan, 5:13-21, 10:31-11:2 – machine learning sentiment analysis. 13:4-30 – sentiment tagging of sentences);
obtain an indication of overall sentiment for a plurality of data segments represented by the text data (Neervannan, Fig. 3 with 5:46-6:7 – aggregate sentiment display. Figs. 3 and 7 with 14:11-33 - Heat map aggregate sentiment display); and
provide the indication of the overall sentiment to a device, together with a selectable option to retrieve one or more of the data segments having an indication of sentiment corresponding to the indication of the overall sentiment (Neervannan, Fig. 3 with 5:46-6:7 – aggregate sentiment display. Figs. 3 and 7 with 14:11-33 - Heat map aggregate sentiment display. Fig. 5A-5B with 13:31-14:33 – sentiments can be selected and identified within the user interface. 3:59-4:3 – display).
Regarding claim 2, Neervannan discloses the elements of claim 1 above, and further discloses wherein obtaining an indication of overall sentiment for the plurality of data segments represented by the text data includes: providing the plurality of data segments represented by the text data to the machine learning system; and obtaining, as output of the machine learning system, an indication of a sentiment associated with the plurality of data segments represented by the text data (Nervannan, 5:13-21, 10:31-11:2 – machine learning sentiment analysis. 13:16-30 – sentiment tagging of sentences).
Regarding claim 3, Neervannan discloses the elements of claim 1 above, and further discloses wherein the data segments represent sentences (Nervannan, 5:13-21, 10:31-11:2 – machine learning sentiment analysis. 13:16-30 – sentiment tagging of sentences).
Regarding claim 4, Neervannan discloses the elements of claim 1 above, and further discloses wherein the instructions further configure the processor to: receive a selection of the selectable option to retrieve the one or more data segments having the indication of sentiment corresponding to the indication of the overall sentiment; in response to receiving the selection of the selectable option, retrieving the data segments having an indication of sentiment corresponding to the indication of the overall sentiment; and providing one or more of the retrieved data segments to the device (Nervannan, Fig. 7 with 14:11-34 – overall sentiment can correspond to positive, negative, or neutral, as shown by the color mapping. Fig. 6 with 13:31-14:10 – individual data segments corresponding to the positive outlook can be selected. For example, statements with positive outlook can be selected to be highlighted for a document corresponding to a sector score with overall positive outlook. 5:22-45 – document level sentiment score).
Regarding claim 5, Neervannan discloses the elements of claim 4 above, and further discloses wherein the one or more of the retrieved data segments are provided on a user interface of the device and wherein the user interface includes a selectable option to filter the retrieved data segments based on a topic (Nervannan, Fig. 6 with 13:31-14:10 – topic can be selected to highlight statements related to the topic), and
wherein the instructions further configure the processor to: receive, via the selectable option to filter the retrieved data segments based on a topic, a filter parameter representing one or more topics; and in response to receiving the filter parameter, filter the retrieved data segments based on the filter parameter and updating the user interface (Nervannan, Fig. 6 with 13:31-14:10 – additional parameters can be set beyond the topic, including filtering boiler plate, sentiment, tense, recurring sentences, and newness. The document is displayed in according to the user selections).
Regarding claim 6, Neervannan discloses the elements of claim 1 above, and further discloses wherein the instructions further configure the processor to: provide at the device, a selectable option to retrieve one or more of the data segments having an indication of sentiment contrary to the indication of the overall sentiment; receive a selection of the selectable option to retrieve the one or more data segments having the indication of sentiment contrary to the indication of the overall sentiment; in response to receiving the selection of the selectable option, retrieve the data segments having an indication of sentiment contrary to the indication of the overall sentiment; and provide one or more of the retrieved data segments to the device (Nervannan, Fig. 7 with 14:11-34 – overall sentiment can correspond to positive, negative, or neutral, as shown by the color mapping. Fig. 6 with 13:31-14:10 – individual data segments corresponding to the negative outlook can be selected. For example, statements with negative outlook can be selected to be highlighted for a document corresponding to a sector score with overall positive outlook. 5:22-45 – document level sentiment score).
Regarding claim 7, Neervannan discloses the elements of claim 1 above, and further discloses wherein the text data is a transcript of a call (Nervannan, 2:52-3:13 – investor call and presentation transcripts).
Regarding claim 8, Neervannan discloses the elements of claim 1 above, and further discloses wherein the text data is a transcript of a transcript of a presentation (Nervannan, 2:52-3:13 – investor call and presentation transcripts).
Regarding claim 9, Neervannan discloses the elements of claim 1 above, and further discloses wherein the indication of overall sentiment indicates one of: a positive overall sentiment, a negative overall sentiment, and a neutral overall sentiment (Nervannan, Fig. 7 with 14:11-34 – overall sentiment can correspond to positive, negative, or neutral, as shown by the color mapping).
Regarding claim 10, Neervannan discloses the elements of claim 1 above, and further discloses wherein the plurality of data segments represented by the text data form all of the text data (Nervannan, Fig. 7 with 14:11-33 – the heat map and scores are shown for the documents. The sentiment scores are based on the sentiments in the documents. The user can filter, or not, on the topic, recurrence, tenses, etc).
Regarding claim 11, Neervannan discloses the elements of claim 1 above, and further discloses wherein the plurality of data segments represented by the text data are a subset of all of the number of data segments obtained through the segmentation (Nervannan, Fig. 7 with 14:11-33 – the heat map and scores are shown for the documents. The sentiment scores are based on the sentiments in the documents. The user can filter, or not, on the topic, recurrence, tenses, etc.. The sentiment score is update to reflect the seleections. 13:31-14:10 – boilerplate and recurring sentences can be hidden from the highlighted view).
Regarding claim 13, Neervannan discloses a computer-implemented method comprising (Neervannan, 3:14-4:3 – computers with processors executing instructions stored in hardware memory):
performing segmentation on text data to generate a number of data segments (Neervannan, 6:17-7:10, 9:20-46 – splitting the text into sentence segments that are statements);
providing each of the data segments to a machine learning system; obtaining, as output of the machine learning system, an indication of a sentiment associated with each of the data segments (Nervannan, 5:13-21, 10:31-11:2 – machine learning sentiment analysis. 13:16-30 – sentiment tagging of sentences);
obtaining an indication of overall sentiment for a plurality of data segments represented by the text data (Neervannan, Fig. 3 with 5:46-6:7 – aggregate sentiment display. Figs. 3 and 7 with 14:11-33 - Heat map aggregate sentiment display); and
providing the indication of the overall sentiment to a device, together with a selectable option to retrieve one or more of the data segments having an indication of sentiment corresponding to the indication of the overall sentiment (Neervannan, Fig. 3 with 5:46-6:7 – aggregate sentiment display. Figs. 3 and 7 with 14:11-33 - Heat map aggregate sentiment display. Fig. 5A-5B with 13:31-14:33 – sentiments can be selected and identified within the user interface. 3:59-4:3 – display).
Regarding claim 14, Neervannan discloses the elements of claim 13 above, and further discloses wherein obtaining an indication of overall sentiment for the plurality of data segments represented by the text data includes: providing the plurality of data segments represented by the text data to the machine learning system; and obtaining, as output of the machine learning system, an indication of a sentiment associated with the plurality of data segments represented by the text data (Nervannan, 5:13-21, 10:31-11:2 – machine learning sentiment analysis. 13:16-30 – sentiment tagging of sentences).
Regarding claim 15, Neervannan discloses the elements of claim 13 above, and further discloses wherein the data segments represent sentences (Nervannan, 5:13-21, 10:31-11:2 – machine learning sentiment analysis. 13:16-30 – sentiment tagging of sentences).
Regarding claim 16, Neervannan discloses the elements of claim 13 above, and further discloses receiving a selection of the selectable option to retrieve the one or more data segments having the indication of sentiment corresponding to the indication of the overall sentiment; in response to receiving the selection of the selectable option, retrieving the data segments having an indication of sentiment corresponding to the indication of the overall sentiment; and providing one or more of the retrieved data segments to the device (Nervannan, Fig. 7 with 14:11-34 – overall sentiment can correspond to positive, negative, or neutral, as shown by the color mapping. Fig. 6 with 13:31-14:10 – individual data segments corresponding to the positive outlook can be selected. For example, statements with positive outlook can be selected to be highlighted for a document corresponding to a sector score with overall positive outlook. 5:22-45 – document level sentiment score).
Regarding claim 17, Neervannan discloses the elements of claim 16 above, and further discloses wherein the one or more of the retrieved data segments are provided on a user interface of the device and wherein the user interface includes a selectable option to filter the retrieved data segments based on a topic (Nervannan, Fig. 6 with 13:31-14:10 – topic can be selected to highlight statements related to the topic), and
wherein the method further includes: receiving via the selectable option to filter the retrieved data segments based on a topic, a filter parameter representing one or more topics; and in response to receiving the filter parameter, filtering the retrieved data segments based on the filter parameter and updating the user interface (Nervannan, Fig. 6 with 13:31-14:10 – additional parameters can be set beyond the topic, including filtering boiler plate, sentiment, tense, recurring sentences, and newness. The document is displayed in according to the user selections).
Regarding claim 18, Neervannan discloses the elements of claim 13 above, and further discloses providing at the device, a selectable option to retrieve one or more of the data segments having an indication of sentiment contrary to the indication of the overall sentiment; receiving a selection of the selectable option to retrieve the one or more data segments having the indication of sentiment contrary to the indication of the overall sentiment; in response to receiving the selection of the selectable option, retrieving the data segments having an indication of sentiment contrary to the indication of the overall sentiment; and providing one or more of the retrieved data segments to the device (Nervannan, Fig. 7 with 14:11-34 – overall sentiment can correspond to positive, negative, or neutral, as shown by the color mapping. Fig. 6 with 13:31-14:10 – individual data segments corresponding to the negative outlook can be selected. For example, statements with negative outlook can be selected to be highlighted for a document corresponding to a sector score with overall positive outlook. 5:22-45 – document level sentiment score).
Regarding claim 19, Neervannan discloses the elements of claim 13 above, and further discloses wherein the text data is a transcript of a call (Nervannan, 2:52-3:13 – investor call and presentation transcripts).
Regarding claim 20, Neervannan discloses the elements of claim 13 above, and further discloses wherein the text data is a transcript of a transcript of a presentation (Nervannan, 2:52-3:13 – investor call and presentation transcripts).
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) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Neervannan in view of Trzyna.
Regarding claim 12, Neervannan discloses the elements of claim 1 above, and further discloses wherein the indication of overall sentiment is a summary of an overall sentiment and wherein the summary is a predefined
However, Neervannan appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Trzyna discloses an LLM summarization of a transcript (Trzyna, Abstract), including
wherein the indication of overall sentiment is a summary of an overall sentiment and wherein the summary is a predefined maximum length (Trzyna, ¶0028, ¶0067 – requirement to limit the summarization to a maximum number of words. The summarization can include sentiment summarization).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the sentiment of Neervannan to include a length-limited summary of the sentiment based on the teachings of Trzyna. The motivation for doing so would have been to provide the user with additional summarization context while enabling the user to digest the additional information quickly and conveniently.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. References are at least relevant as indicated in the corresponding summary.
Childress (US Patent Application Publication 2024/0020715) – LLM sentiment evaluation and presentation. Overall sentiment is presented as well as more granular results.
Beaver (US Patent Application Publication 2020/0311348) – per-channel sentiment evaluation by machine learning.
Ramamohan (US Patent Application Publication 2025/0080605) – Machine learning sentiment analysis and demographic segmentation.
Litvin (US Patent Application Publication 2021/0271864) – Machine learning sentiment analysis and demographic segmentation.
Renard et al. (US Patent Application Publication 2019/0215249) – Machine learning call sentiment analysis and scoring.
Zhu et al. (US Patent Application Publication 2023/0056772) – NLP-based sentence-level sentiment analysis and scoring.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL W PARCHER whose telephone number is (303)297-4281. The examiner can normally be reached Monday - Friday, 9:00am - 5:00pm, Mountain Time.
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/DANIEL W PARCHER/Primary Examiner, Art Unit 2174
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