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 .
Claims 1-5, 7-10, 12-16, 20, 21, and 24-26 are pending.
Claims 1-2, 8-10, 13, 15-16 and 20 are amended.
Claims 6, 11, and 23 are cancelled and claims 17-19 were previously cancelled. Claims 24-26 are added.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission has been entered.
Response to Arguments
Applicant’s arguments with respect to Section 112 have been fully considered and are not persuasive. While Applicant correctly notes that the specification does not state that the features described cannot be performed in real time, that is not the test under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, written description. The ability of a person skilled in the art to design a system that would function as claimed relates to enablement, not written description and does not amount to possession of the claimed invention by the inventor(s). The benefits of real time review processing as opposed to batch processing and generation of host review database is understandable ability of a person skilled in the art. The Section 112 rejection is maintained, but updated below to address the new claim amendments and new claims.
Applicant's arguments filed with respect to Section 101 have been fully considered and are not persuasive. Applicant argues that the claims are directed to any abstract idea, but the abstract idea has been clearly set forth in the claim rejection. Applicant further argues that the claims address the technical problem of identifying reviews that discuss the host. However, the identification of reviews that discuss a host is not a technical problem. Instead, it is basic reading comprehension, a human mental process or organized human activity.
While the claims have been amended to deep learning to apply attention or self attention, that amounts to using off the shelf technology, including the BERT model discussed in previously cited Malon (U.S. 2022/0327586). “BERT is a deep learning language model designed to improve the efficiency of natural language processing (NLP) tasks. Google researchers introduced the BERT model in a 2018 paper titled “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”.” See “What Is the BERT Model and How Does It Work?” retrieved from https://www.coursera.org/articles/bert-model. “BERT is an "encoder-only" transformer architecture. At a high level, BERT consists of 4 modules… [including the} Encoder: a stack of Transformer blocks with self-attention, but without causal masking.” See BERT (language model), Architecture, retrieved from https://en.wikipedia.org/wiki/BERT_(language_model). The use of such off the shelf technology for its intended purpose amounts to providing technological environment or tools to perform the abstract. See Appeal 2025-002132, Application 17/868,572 (“The description of the additional elements evidences they are generic elements used as tools to perform the abstract idea. The machine learning model is a generic language model such as a masked language model or a bi-directional encoding language model (BERT).”, page 11). As the deep learning and other additional elements are recited at a high level of generality, the arguments are not persuasive. The rejection has been updated to address the amended claims.
Applicant’s arguments with respect to Section 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. With respect to the argument regarding new limitation to “using a deep learning model to apply attention or self-attention” it is noted that the rejection has been updated to address the amended language using previously cited Malon (U.S. 2022/0327586).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-16 and 20-23 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1, 10, and 16 have been amended to recite “performing operations, in real time from detecting the selection…”. However, the specification makes no mention of any process being done in real time. In fact, in paragraph [0074] and [0084], which are duplicate paragraphs, that processes may be driven by cost and time considerations to be performed at different times based on data stored in memory. As such, there does not appear to be any support for the idea that any operations are performed in real time, as recited in the amended claims, in the specification as filed. Appropriate correction is required.
Claims 2-9, 11-15, and 20-23 are rejected for incorporating at least the issues of the claims from which they depend.
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 1-16 and 20-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Representative claim 10 recites “generating, from each review of a set of reviews associated with the host, a set of segments that each comprises a predefined number of characters, …. detect, relationships in each segment of the set of segments for a given review to generate a host relevancy score for each segment, the host relevancy score indicating a level of detail about the host described in the segment of the review, causing display, …, of a portion of each review with information about the host in an order based on the host relevancy score for each review in the set of reviews”. Therefore, the claim as a whole is directed to “Review relevancy ranking”, which is an abstract idea because it is method of organizing human activity, including at least commercial interactions (including advertising, marketing or sales activities or behaviors; business relations) and a mental process, including concepts performed in the human mind (including an observation, evaluation, judgment, opinion). “Review relevancy ranking” is considered to be is a method of organizing human activity because accessing review data and analyzing such review data for relevance is a human process conducted by advertising personnel for selection of reviews for use in advertising, for analyzing customer sentiment, and for determining the scope of publicly available information. As such, the claim is directed to an abstract idea.
“Review relevancy ranking” is considered to be is a mental process because the processes steps may be performed mentally. The “mental processes” abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. In this case, the claimed processes may be performed mentally with review of written or oral review of materials from others, analysis of that data, and presentation of results. As such, the claim is directed to an abstract idea.
This judicial exception is not integrated into a practical application. In particular, claim 10 recites the following additional element(s): detecting selection in a graphical user interface of a host user profile associated with a host for one or more listings in a listing network platform, performing operations, in real time from detecting the selection; using a deep learning model to apply attention or self-attention to detect relationships in inputting each segment of the set of segments for a given review; and causing display, on the graphical user interface, of a portion of reviews. Claim 1 additionally recites at least one processor; at least one memory component storing instructions and claim 16 recites non-transitory computer-readable storage medium storing instructions. These additional elements individually or in combination do not integrate the exception into a practical application. That is, the recitations of additional elements amount merely reciting the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). The claims recite generic computer components and software used to perform the abstract idea, but recite no improvement in technology and address not technological problem. For example,
Rather, it amounts to reciting the use of a commonly available model to perform its intended purpose. As such, the additional elements also do no more than generally link the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). 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. Claim 10 is directed to an abstract idea.
Claim 10 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, individually and in combination recite, are merely being used to apply the abstract idea to a technological environment. As noted above, the claims recite generic computer components and software used to perform the abstract idea, but recite no improvement in technology and address not technological problem. Accordingly, claim 10 is ineligible.
Claims 1 and 16 recited substantially similar features to those recited in representative claim 1 and are ineligible based on substantially the same reasons.
Dependent claims 2-9, 11-15, and 17-20 merely further limit the abstract idea and are thereby considered to be ineligible.
Dependent claim 2 further limits the abstract idea of “Review relevancy ranking” by introducing the element of the set of reviews is displayed within a profile associated with the host user, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 2 is also non-statutory subject matter.
Dependent claim 3 further limits the abstract idea of “Review relevancy ranking” by introducing the element of display of the profile associated with the host user further comprises a set of prompt questions about the host user, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 3 is also non-statutory subject matter.
Dependent claim 4 further limits the abstract idea of “Review relevancy ranking” by introducing the element of the prompt questions are ranked according to a predefined set of ranking rules, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 4 is also non-statutory subject matter.
Dependent claim 5 further limits the abstract idea of “Review relevancy ranking” by introducing the element of the profile of the host user is ranked based on a size of the set of prompt questions displayed on the profile, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 5 is also non-statutory subject matter.
Dependent claims 6 and 11 further limit the abstract idea of “Review relevancy ranking” by introducing the element of each segment in the set of segments comprises a predefined number of characters, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claims 6 and 11 are also non-statutory subject matter.
Dependent claim 7 further limits the abstract idea of “Review relevancy ranking” by introducing the element of each review in the set of reviews is associated with listing data from a plurality of listing data, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 7 is also non-statutory subject matter.
Dependent claim 12 further limits the abstract idea of “Review relevancy ranking” by introducing the element of each review in the set of reviews is associated with a same listing data, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 12 is also non-statutory subject matter.
Dependent claims 8, 13, and 20 further limits the abstract idea of “Review relevancy ranking” by introducing the element of for each review in the set of reviews: identifying a sentiment associated with the review by analyzing the review …, the sentiment comprising at least one of a positive sentiment, a negative sentiment or a neutral sentiment; determining a rating associated with the host user; based on the rating, identifying a target sentiment, the target sentiment correlating with the rating; determining a second rank score for the review based on the identified sentiment of the review matching the target sentiment; and determining an average rank score based on an average of the first rank score and the second rank score; and wherein causing display of the set of reviews in an order is based on the average rank score for each review in the set of reviews, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claims 8, 13, and 20 are also non-statutory subject matter.
Dependent claims 9 and 15 further limit the abstract idea of “Review relevancy ranking” by introducing the element of for each review in the set of reviews: identifying a name associated with the host user by analyzing the review….; determining a third rank score for the review based on a positive identification of the name associated with the host user in the review; determining a revised average rank score based on an average of the first rank score, the second rank score and the third rank score; and wherein causing display of the set of reviews in an order is based on the revised average rank score for each review in the set of reviews, on the graphical user interface of the computing device, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claims 9 and 15 are also non-statutory subject matter.
Dependent claim 14 further limits the abstract idea of “Review relevancy ranking” by introducing the element of a high rating associated with the host user correlates with a positive target sentiment and wherein a low rating associated with the host user correlates with a negative target sentiment, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 14 is also non-statutory subject matter.
Dependent claim 21 further limits the abstract idea of “Review relevancy ranking” by introducing the element of the set of reviews is displayed horizontally on the graphical user interface, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 21 is also non-statutory subject matter.
Dependent claim 22 further limits the abstract idea of “Review relevancy ranking” by introducing the element of the set of reviews is horizontally scrollable in the graphical user interface, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 22 is also non-statutory subject matter.
Dependent claim 23 further limits the abstract idea of “Review relevancy ranking” by introducing the element of causing display of the set of reviews comprises causing a portion of each review with information about the host to be displayed for each review in the graphical user interface, which does not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Therefore, dependent claim 23 is also non-statutory subject matter.
Dependent claims 2-9, 11-15, and 20-23 also do not integrated into a practical application. The dependent claims recite using a sentiment machine learning model and using a name detector machine learning model. These additional elements merely generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. As such, the recitations of additional elements individually or in combination do not integrate the exception into a practical application, but rather, the recitation of any recitations of additional elements amounts to merely reciting the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). The dependent claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a computing system is merely being used to apply the abstract idea to a technological environment. That is, the claims provide no practical limits or improvements to any technology. Accordingly, dependent claims 2-9, 11-15, and 20-23 are also ineligible.
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.
Claims 1, 2, 7, 10, 12, 16, and 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20070078670 to Dave et al. in view of U.S. Patent Application Publication No. 20220327586 to Malon.
With regards to claims 1, 10, and 16, Dave et al. teaches
at least one processor; at least one memory component storing instructions that, when executed by the at least one processor (paragraph [0093], “The system 700 typically includes one or more processing units (CPU's) 702, one or more network or other communications interfaces 710, memory 712, and one or more communication buses 714 for interconnecting these components. The system 700 optionally may include a user interface 704 comprising a display device 706 and a keyboard/mouse 708. The memory 712 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other random access solid state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory 712 may optionally include one or more storage devices remotely located from the CPU(s) 702.”), cause the at least one processor to perform operations comprising:
in response to detecting selection in a graphical user interface of a host user profile associated with a host for one or more listings in a listing network platform, performing operations, in real time from detecting the selection (paragraph [0034], “Users may request from the reviews engine reviews information for a subject, such as a product, service, or provider, through a client 102. For example, the user may click on a link, in a web page displayed on client 102, which triggers transmission of a request to the reviews engine 106. An exemplary process for handling such a request is described below.”; paragraph [0035], “Via clients 102, a user may request, from the reviews engine 106, a reviews summary for a subject or a class of subjects. The reviews engine 106 receives a request from a client 102 for a reviews summary for a subject (202). Reviews for the subject that are stored in the reviews repository 112 are identified (204). A subset of the identified reviews is selected (206). A response including content from the selected subset is generated (208). The response is transmitted to the client 102 (210). The client 102, upon receiving the response, renders the response in a client application, such as a web browser, for presentation to the user.”), comprising:
generating, from each review of a set of reviews associated with the host (paragraph [0024], “The subject of a review is a particular entity or object to which the content in the review provides comments, evaluation, opinion, or the like. In some embodiments, a subject of a review may be classified according to the type of subject. Examples of subject types include products, services, providers of products, providers of services, and so forth. A review may be directed to a class of subjects.”), a set of segments that each comprises a predefined number of characters (paragraph [0049], “The quality score may be based on one or more predefined factors. In some embodiments, the predefined factors include the length of the review, the lengths of sentences in the review, values associated with words in the review, and grammatical quality of the review.”; paragraph [0051], “With regard to the length of the review, reviews that are not too long and not too short are favored. Short reviews (e.g., a few words) tend to be uninformative and long reviews (e.g., many paragraphs) tend to be not as readable as a shorter review. In some embodiments, the review length may be based on a word count. In some other embodiments, the review length may be based on a character count or a sentence count. The review length sub-score may be based on a difference between the length of the review and a predefined “optimal” review length.”, while Dave et al. teaches a number of words and a number of sentences, a number of characters is one of a finite number of identified, predictable potential solutions to the recognized need or problem of limiting review length); and
…. generate a host relevancy score for each segment, the host relevancy score indicating a level of detail about the host described in the segment of the review (paragraph [0025], “A rating may be associated with a review and stored along with the review. The rating (or “rating score”) represents a score, on a predefined scale, for the subject (or class of subjects) of the review. The format of a rating may be a numerical value or any non-numerical format that can be mapped to a numerical value.”; paragraph [0040], “An overall rating score is determined for the subject (308). The overall rating score may be a mathematical combination of the collective ratings for the subject given by the review sources. In some embodiments, the overall rating score is a weighted average of the collective ratings. The weights are based on the number of reviews in the corpus that are included in each source.”; paragraph [0053], “In some embodiments, tables of IDF values are generated for reviews of each type. For example, a table of IDF values is generated for all product reviews; a table is generated for all product provider reviews, and so forth. That is, the set of texts used for determining the table of IDF values for product reviews are all product reviews in the reviews repository 112; the set of texts used for determining the table of IDF values for product provider reviews are all product provider reviews in the reviews repository 112, and so forth. Each subject type has its own IDF values table because words that are valuable in reviews for one subject type may not be as valuable in reviews for another subject type.”, where a host is a provider); and
causing display, on the graphical user interface, of a portion of each review with information about the host in an order based on the host relevancy score for each review in the set of reviews (paragraph [0035], “A response including content from the selected subset is generated (208). The response is transmitted to the client 102 (210). The client 102, upon receiving the response, renders the response in a client application, such as a web browser, for presentation to the user.”), but fails to explicitly teach the use of deep learning model to apply attention or self-attention to detect relationships. However, Malon teaches
using a deep learning model to apply attention or self-attention (paragraph [0054], “At block 320, a pretrained masked language model, T0, can be inputted to the claim generator training method 350. In one or more embodiments, a pretrained transformer model, T, that has been pretrained for masked language modeling, such as BERT, can be fine-tuned. The vocabulary for such a model includes tokens for common words and pieces of words, and several special tokens used in training, including a classification token and a separator token.”, where the BERT model is a deep learning model and the encoder in BERT applies self-attention) to detect relationships in each segment of the set of segments for a given review to generate a host relevancy score for each segment (paragraph [0005], “The method includes performing a frequency analysis on an inputted list of product reviews for a single item and an inputted corpus of reviews for a product category containing the single item to identify one or more frequent phrases. The method further includes fine tuning a pretrained transformer model to produce a trained neural network claim generator model, T1, and generating a trained neural network opposing claim generator model based on the trained neural network claim generator model.”),, the host relevancy score indicating a level of detail about the host described in the segment of the review the transformer machine learning model trained on a labeled dataset of review data (paragraph [0039], “At block 220, reviews of a product containing the key word or phrase can be inputted to the claim generator. Sentences r(1); . . . ; r(n) containing the key word or phrase are extracted from the product's reviews.”; paragraph [0040], “At block 230, the part of speech or type of word or phrase that has been inputted by the user or selected based on frequency can be determined in order to determine which template to apply. Existing systems, such as Spacy, that can label words of sentences with their part of speech can be used. For each part of speech or phrase type, zero or more summary templates, f, can be defined in block 230 for use in block 240. Each summary template, f, quotes the key word or phrase, ki, or a word or phrase derived from ki, and has zero or one positions which are masked and are to be filled in with a word. In one or more embodiments, frequent phrases for the product are used to fill in the templates.”).
This part of Malon is applicable to the system of Dave et al. et al. as they both share characteristics and capabilities, namely, they are directed to data processing of review data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Dave et al. et al. to include the deep learning with self-attention as taught by Malon. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Dave et al. et al. in order to allow for summarizing diverse and contrasting opinions found in reviews that use differing language (see paragraphs [0016]-[0018] of Malon).
With regards to claims 2, Dave et al. teaches: the portion of each review is displayed within a profile associated with the host user (paragraph [0034], “Users may request from the reviews engine reviews information for a subject, such as a product, service, or provider, through a client 102. For example, the user may click on a link, in a web page displayed on client 102, which triggers transmission of a request to the reviews engine 106. An exemplary process for handling such a request is described below.”).
With regards to claim 7, Dave et al. teaches: each review in the set of reviews is associated with listing data from a plurality of listing data (paragraph [0054], “The reviews repository 112 stores reviews and associated ratings. The reviews repository 112, also stores the subject or class of subjects and the subject type (i.e., whether the subject or class of subjects is a product, product provider, etc.) for each review. The reviews repository 112 may also store the source, the author, and the date for each review. In some embodiments, a review and rating may be associated, in the reviews repository 112, with one or more evaluations of the review and rating itself.”).
With regards to claim 12, Dave teaches: each review in the set of reviews is associated with a same listing data (paragraph [0024], “In some embodiments, a subject of a review may be classified according to the type of subject. Examples of subject types include products, services, providers of products, providers of services, and so forth. A review may be directed to a class of subjects. A class of subjects includes a plurality of particular entities or objects that share a common trait, characteristic, or feature. For example, a particular product line may be a class of subjects that may be the subject of a review. As another example, all products having a particular brand may be a class of subjects that may be the subject of a review.”; paragraph [0032], “The reviews repository 112 stores reviews and associated ratings. The reviews repository 112, also stores the subject or class of subjects and the subject type (i.e., whether the subject or class of subjects is a product, product provider, etc.) for each review.”).
With regards to claim 24, Dave teaches: where in the portion of each review highlights features about the host (paragraph [0055], “Separate dictionaries may be defined for different subject types, as different words may be valuable for use in reviews regarding different subject types. For example, there may be a dictionary of valuable words for reviews where the subject is a product and another dictionary of valuable words for reviews where the subject is a provider.”).
With regards to claim 25, Dave teaches: the portion of each review is a selectable user interface element (paragraph [0034], “Users may request from the reviews engine reviews information for a subject, such as a product, service, or provider, through a client 102. For example, the user may click on a link, in a web page displayed on client 102, which triggers transmission of a request to the reviews engine 106. An exemplary process for handling such a request is described below.”), and the operations further comprise:
detecting selection of the selectable user interface element (paragraph [0034], “Users may request from the reviews engine reviews information for a subject, such as a product, service, or provider, through a client 102. For example, the user may click on a link, in a web page displayed on client 102, which triggers transmission of a request to the reviews engine 106. An exemplary process for handling such a request is described below.”); and
based on detecting selection of the selectable user interface element, causing display of an overlay window that displays an entirety of a review associated with a portion of the review (paragraph [0036], “The generated response is a document that is transmitted to a client 102 for rendering and presentation to a user. The response document may include a review summary for the subject. The reviews summary includes information such as the overall rating for the subject, further details of which are described below in relation to FIG. 3.”; paragraph [0037], “The reviews summary also includes a reviews sample. In some embodiments, the reviews sample may include the full contents of at least some of the selected reviews. For text-based reviews, the full content of a review is the entire text of the review. For video based reviews, the full content of a review is the full video clip of the review. In some other embodiments, the reviews sample may include snippets of at least some of the selected reviews, further details of which are described below, in relation to FIG. 6. It should be appreciated, however, that in some embodiments the reviews sample may include both the full content of some selected reviews and snippets of other selected reviews. The review sample may also include one or more links to the sources of the reviews for which the full contents or snippets are included in the reviews sample.”).
With regards to claim 26, Dave teaches: the set of reviews are associated with a single listing or multiple listings (paragraph [0024], “The subject of a review is a particular entity or object to which the content in the review provides comments, evaluation, opinion, or the like. In some embodiments, a subject of a review may be classified according to the type of subject. Examples of subject types include products, services, providers of products, providers of services, and so forth. A review may be directed to a class of subjects. A class of subjects includes a plurality of particular entities or objects that share a common trait, characteristic, or feature. For example, a particular product line may be a class of subjects that may be the subject of a review. As another example, all products having a particular brand may be a class of subjects that may be the subject of a review.”; paragraph [0032], “The reviews repository 112 stores reviews and associated ratings. The reviews repository 112, also stores the subject or class of subjects and the subject type (i.e., whether the subject or class of subjects is a product, product provider, etc.) for each review. The reviews repository 112 may also store the source, the author, and the date for each review.”).
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20070078670 to Dave et al. in view of U.S. Patent Application Publication No. 20220327586 to Malon as applied to claims 11, 2, 7, 10, 12, 16, and 24-26, further in view of U.S. Patent Application Publication No. 20200090233 to D'alfonso et al.
With regards to claims 3, modified Dave et al. fails to explicitly teach, but D'alfonso et al. teaches: display of the profile associated with the host user further comprises a set of prompt questions about the host user (paragraph [0014], “First, a user may be prompted to specify a category of subjects, and then, the user may specify his or her preferences, including requirements and interests, relating to subjects that fall within the selected category. Reviews of subjects may then be processed to eliminate any subjects that do not comply with a user's requirements for the subject category, and the remaining subjects can be ranked according to the user's interests”).
This part of D'alfonso et al. is applicable to the system of modified Dave et al. et al. as they both share characteristics and capabilities, namely, they are directed to data processing of review data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of modified Dave et al. et al. to include the user selection displays as taught by D'alfonso et al. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Dave et al. et al. in order to allow for providing reviews that meet search criteria and satisfy all of the user's requirement (see paragraphs [0052]-[0054] of D'alfonso et al.).
With regards to claims 4, modified Dave et al. fails to explicitly teach, but D'alfonso et al. teaches: the prompt questions are ranked according to a predefined set of ranking rules (paragraph [0014], “For each category of subjects, a user may provide requirements, which are features that must be satisfied, and interests, which are features that a user would like to see in a given subject”).
This part of D'alfonso et al. is applicable to the system of modified Dave et al. et al. as they both share characteristics and capabilities, namely, they are directed to data processing of review data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of modified Dave et al. et al. to include the user selection displays as taught by D'alfonso et al. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Dave et al. et al. in order to allow for providing reviews that meet search criteria and satisfy all of the user's requirement (see paragraphs [0052]-[0054] of D'alfonso et al.).
With regards to claims 5, modified Dave et al. fails to explicitly teach, but D'alfonso et al. teaches: the profile of the host user is ranked based on a size of the set of prompt questions displayed on the profile (where in the number of data fields determined the specificity of the ranking evaluations, paragraph [0073], “Data relating to processing reviewed subjects may include any desired format and arrangement, and may include any quantity of any types of fields of any size to store any desired data. The fields may indicate the presence, absence, actual values, or any other desired characteristics of the data of interest (e.g., quantity, value ranges, etc.).”).
This part of D'alfonso et al. is applicable to the system of modified Dave et al. et al. as they both share characteristics and capabilities, namely, they are directed to data processing of review data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of modified Dave et al. et al. to include the user selection displays as taught by D'alfonso et al. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Dave et al. et al. in order to allow for providing reviews that meet search criteria and satisfy all of the user's requirement (see paragraphs [0052]-[0054] of D'alfonso et al.).
Claims 8, 9, 13-15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20070078670 to Dave et al. in view of U.S. Patent Application Publication No. 20220327586 to Malon as applied to claims 1, 2, 7, 10, 12, 16, and 24-26, further in view of U.S. Patent Application Publication No. 20190361987 to Qiao et al.
With regards to claims 8, 13, and 20, Dave et al. teaches analyzing the content of a review, but fails to discuss the details of the sentiment analysis as claimed. However Qiao et al. teaches: for each review in the set of reviews:
identifying a sentiment associated with the review by analyzing the review using a sentiment machine learning model, the sentiment comprising at least one of a positive sentiment, a negative sentiment or a neutral sentiment (paragraph [0038], “The sentiment may gauge the reviewer's evaluation or judgment of the reviewed item or service. For example, the sentiment may indicate whether the sentence of the review is positive, negative, or neutral in sentiment, or may indicate the sentiment on a scale of, for example, −1 to +1 (where a negative value indicates negative sentiment and a positive value indicates positive sentiment).”);
determining a rating associated with the host user (paragraph [0030, “The review ranking module 228 may also rank the excerpts extracted by the excerpt extraction module. In one embodiment, the ranking of the excerpts is based on at least one of: syntax, grammar, sentiment, rating by a reviewer, overall rating of the item by reviewers, number of positive reviews and number of negative reviews.”);
based on the rating, identifying a target sentiment, the target sentiment correlating with the rating (paragraph [0062], “. Aspects are attributes or features of an item discussed in reviews upon which the reviewer expresses an opinion. Aspects may also be opinion targets. …. In one embodiment, to aggregate the aspect candidates and associated opinions, the frequency of occurrence of each of the aspect candidates and/or aggregate sentiments associated with each of the aspect candidates may be determined and analyzed.”);
determining a second rank score for the review based on the identified sentiment of the review matching the target sentiment (paragraph [0032], “The machine learning system may determine a review helpfulness score based, for example, on feedback obtained from a user(s) of the review. The user(s) of the review may be a plurality of users that have viewed the review on their client devices and have provided feedback on the review. For example, a user may mark a review as helpful or not helpful, or may mark a review based on a helpfulness scale (for instance, on a scale of zero to ten). The feedback may be obtained in response to the presentation of the review to the user. Feedback from multiple users may be used to produce a cumulative helpfulness score. For example, the feedback for a review from multiple users may be averaged to produce the cumulative helpfulness score.”); and
determining an average rank score based on an average of the first rank score and the second rank score (paragraph [0032], “The machine learning system may determine a review helpfulness score based, for example, on feedback obtained from a user(s) of the review. The user(s) of the review may be a plurality of users that have viewed the review on their client devices and have provided feedback on the review. For example, a user may mark a review as helpful or not helpful, or may mark a review based on a helpfulness scale (for instance, on a scale of zero to ten). The feedback may be obtained in response to the presentation of the review to the user. Feedback from multiple users may be used to produce a cumulative helpfulness score. For example, the feedback for a review from multiple users may be averaged to produce the cumulative helpfulness score.”); and
wherein causing display of the set of reviews in an order is based on the average rank score for each review in the set of reviews (paragraph [0025], “The review summary may be obtained from the review ranking module 228. The interface may also display an identification of one or more reviews obtained in response to a query; and an identified review may be selected by the user in order to display the selected review via the interface. The interface may also display a blurb in association with an item.”).
This part of Qiao et al. is applicable to the system of Dave et al. as they both share characteristics and capabilities, namely, they are directed to data processing of review data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Dave et al. to include the sentiment analysis as taught by Qiao et al. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Dave et al. in order to process reviews for helpfulness in order to select the most relevant review data for presentation (see paragraphs [0001]-[0003] of Qiao et al.).
With regards to claims 9 and 15, Dave et al. fails to explicitly teach, but Qiao et al. teaches: for each review in the set of reviews:
identifying a name associated with the host user by analyzing the review using a name detector machine learning model (paragraph [0044], “To generate the review summary, method 400 starts identifying a plurality of topics for the item at Block 410. A topic model is used to identify a unique set of semantic topics for each product and service associated with a selected review.”; paragraph [0068], “The user interface 1100 may be displayed in response to a query for a type of item (e.g., video games). As shown in user interface 1100, a list of items of that type of item is displayed. Each of the items in the list of items may include, for example, the title of the item (e.g., name of the video game), an image of the cover art associated with the item (e.g., cover art of the video game), the price of the item, a star rating of the item, and a brief description.”);
determining a third rank score for the review based on a positive identification of the name associated with the host user in the review (paragraph [0020], “A set of reviews is then formed as an extraction pool based on the probability that a review is helpful and the probability that a review represents a particular topic(s). Finally, a ranking algorithm is applied to extract sentences from the pool of reviews based on sentence quality, sentence sentiment, helpfulness scores (such as sentence and review helpfulness scores), and the like.”);
determining a revised average rank score based on an average of the first rank score, the second rank score and the third rank score (where ranking of name as a topic is part of scoring, paragraph [0020], “A set of reviews is then formed as an extraction pool based on the probability that a review is helpful and the probability that a review represents a particular topic(s). Finally, a ranking algorithm is applied to extract sentences from the pool of reviews based on sentence quality, sentence sentiment, helpfulness scores (such as sentence and review helpfulness scores), and the like.”; paragraph [0032], “The machine learning system may determine a review helpfulness score based, for example, on feedback obtained from a user(s) of the review. The user(s) of the review may be a plurality of users that have viewed the review on their client devices and have provided feedback on the review. For example, a user may mark a review as helpful or not helpful, or may mark a review based on a helpfulness scale (for instance, on a scale of zero to ten). The feedback may be obtained in response to the presentation of the review to the user. Feedback from multiple users may be used to produce a cumulative helpfulness score. For example, the feedback for a review from multiple users may be averaged to produce the cumulative helpfulness score.”); and
wherein causing display of the set of reviews in an order is based on the revised average rank score for each review in the set of reviews, on the graphical user interface of the computing device (paragraph [0025], “The review summary may be obtained from the review ranking module 228. The interface may also display an identification of one or more reviews obtained in response to a query; and an identified review may be selected by the user in order to display the selected review via the interface. The interface may also display a blurb in association with an item.”).
This part of Qiao et al. is applicable to the system of Dave et al. as they both share characteristics and capabilities, namely, they are directed to data processing of review data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Dave et al. to include the sentiment analysis as taught by Qiao et al. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Dave et al. in order to process reviews for helpfulness in order to select the most relevant review data for presentation (see paragraphs [0001]-[0003] of Qiao et al.).
With regards to claims 14, Dave et al. fails to explicitly teach, but Qiao et al. teaches: a high rating associated with the host user correlates with a positive target sentiment and wherein a low rating associated with the host user correlates with a negative target sentiment (paragraph [0038], “For example, the sentiment may indicate whether the sentence of the review is positive, negative, or neutral in sentiment, or may indicate the sentiment on a scale of, for example, −1 to +1 (where a negative value indicates negative sentiment and a positive value indicates positive sentiment).”).
This part of Qiao et al. is applicable to the system of Dave et al. as they both share characteristics and capabilities, namely, they are directed to data processing of review data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Dave et al. to include the sentiment analysis as taught by Qiao et al. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Dave et al. in order to process reviews for helpfulness in order to select the most relevant review data for presentation (see paragraphs [0001]-[0003] of Qiao et al.).
Claims 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over U U.S. Patent Application Publication No. 20070078670 to Dave et al. in view of U.S. Patent Application Publication No. 20220327586 to Malon as applied to claims 1, 2, 7, 10, 12, 16, and 24-26, further in view of U.S. Patent Application Publication No. 20180349485 to Carlisle et al.
With regards to claim 21, modified D'alfonso et al. fails to explicitly teach, but Carlisle et al. teaches the set of reviews is displayed horizontally on the graphical user interface (paragraph [0235], “Each content feed 350C may be navigated, populated, and/or otherwise utilized independently of the other content feeds 350C. While three content feeds 350C are illustrated in FIG. 3W, it will be appreciated that any number of content feeds 350C may be included (e.g., two, four, five, etc.). Additionally, each content feed 350C may be arranged horizontally along the bottom of screen 346 (as shown in FIG. 3U), horizontally along the top of screen 346, or vertically along one or both sides of screen 346. The user may be able to shift or rearrange content feeds 350C as desired by, for example, selecting a particular content feed 350C and dragging or moving it to another position or via voice input.”).
This part of Carlisle et al. is applicable to the system of Dave et al. as they both share characteristics and capabilities, namely, they are directed to data processing of review data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Dave et al. to include the content feed displays as taught by Carlisle et al. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Dave et al. to enable users too quickly and efficiently search, find, and consume the resources that they need or desire (see paragraph [0006] of Carlisle et al.).
With regards to claim 22, modified D'alfonso et al. fails to explicitly teach, but Carlisle et al. teaches the set of reviews is horizontally scrollable in the graphical user interface (paragraph [0235], “Each content feed 350C may be navigated, populated, and/or otherwise utilized independently of the other content feeds 350C. While three content feeds 350C are illustrated in FIG. 3W, it will be appreciated that any number of content feeds 350C may be included (e.g., two, four, five, etc.). Additionally, each content feed 350C may be arranged horizontally along the bottom of screen 346 (as shown in FIG. 3U), horizontally along the top of screen 346, or vertically along one or both sides of screen 346. The user may be able to shift or rearrange content feeds 350C as desired by, for example, selecting a particular content feed 350C and dragging or moving it to another position or via voice input.”).
This part of Carlisle et al. is applicable to the system of Dave et al. as they both share characteristics and capabilities, namely, they are directed to data processing of review data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Dave et al. to include the content feed displays as taught by Carlisle et al. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Dave et al. to enable users too quickly and efficiently search, find, and consume the resources that they need or desire (see paragraph [0006] of Carlisle et al.).
Conclusion
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/J.D.S./Examiner, Art Unit 3626
/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626
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