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 § 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 (i.e., changing from AIA to pre-AIA ) 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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-6, 12-17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shaobo (CN114595671) in view of Lina (CN111984767A).
Regarding claim 1, Shaobo discloses a reply content processing method, performed by a computer device comprising [see para. 0002 and figure 5; the generation of recommendation information for the target audience (e.g., content, services, products, etc. provided by merchants or business providers) will inevitably be involved. The recommended information may include, for example, advertisements, promotional slogans, video comments and obtaining the category of an object to be recommended; determining a preset category from a plurality of preset categories that matches the category of the object to be recommended, each of the plurality of preset categories having at least one corresponding recommendation information template; obtaining a recommendation information template from the at least one recommendation information template corresponding to the matching preset category ; which corresponds to media content and user generated interactive data for reply recommendation];
performing encoding processing on description content of the media content and the to-be- replied interactive content to obtain a vectorized representation of each word in the description content and the to-be-replied interactive content [see para. 0040; The BERT model is based on the Transformer, which is the mainstream feature extractor in the field of Neuro-Linguistic Programming (NLP). Of course, any other suitable vectorization model or method can be used to encode the categories of the objects to be recommended, such as the Word2vec model, etc], and the to-be-replied interactive content to obtain a first style category set to which the to-be-replied interactive content belongs [see para. 0058; the various word segmentation techniques can be used to segment text information that meets preset text conditions. For example, when the text information that meets the preset text conditions is in English, word segmentation can be completed simply by using spaces and punctuation marks. When the text information that meets the preset text conditions is in Chinese, the Chinese text can be segmented using matching and statistical methods; which corresponds to recognize categories styles and determining categories based on vector];
performing style recognition based on release party information of the to-be-replied interactive content to obtain a second style category set to which the release party information belongs [see para. 0032, 0037; the categories of the object to be recommended may first be encoded to obtain a first encoding vector; then, among the plurality of preset categories, the preset category corresponding to the second encoding vector that is closest to the obtained first encoding vector is determined as the matching preset category, wherein each of the plurality of preset categories is pre-encoded as a second encoding vector and encoded in the same way as the category of the object to be recommended; which corresponds to recognize secondary categories styles and determining categories vector based analysis];
determining, according to the first style category set and the second style category set, a third style category set to which the to-be-replied interactive content belongs [see para. 0082, 0056; when the selected recommendation information template is a text template with named entity wildcards, the target attribute words (e.g., specific brand names and product names) corresponding to the named entity wildcards of the object to be recommended can be obtained; and the corresponding named entity wildcards in the text template can be replaced with the target attribute words to generate recommendation information for the object to be recommended; which corresponds to form a target category set] ;
determining a style category vector corresponding to each style category in the third style category set [see para. 0038, 0040; the categories of the object to be recommended may first be encoded to obtain a first encoding vector; then, among the plurality of preset categories, the preset category corresponding to the second encoding vector that is closest to the obtained first encoding vector is determined as the matching preset category, wherein each of the plurality of preset categories is pre-encoded as a second encoding vector and encoded in the same way as the category of the object to be recommended. The first and second encoding vectors can typically be, for example, embedding vectors encoded using word embedding techniques "; which corresponds to compute vectors for each category]; and
performing reply word prediction based on the description content, the to-be-replied interactive content, and the style category vector to generate reply content corresponding to each style category [see para 0046; the obtained recommendation information template includes a text template with named entity wildcards. In this case, the target attribute words corresponding to the named entity wildcards of the object to be recommended obtained first, and then the target attribute words used to replace the corresponding named entity wildcards in the text template to generate recommendation information for the object to be recommended. The target attribute words of the object to be recommended refer to the descriptive words corresponding to the attributes that the object to be recommended possesses; which corresponds to generate words for recommendation outputs by filling template]; however, Shaobao fails to explicitly teach obtaining to-be-replied interactive content for media content.
Lina discloses obtaining to-be-replied interactive content for media content [see para. 0038, interaction information with the peer user can be obtained to determine matching general reply information, and the expression style corresponding to the local user can be obtained. Then, the general reply information is converted according to the expression style to obtain interesting reply information, which is then displayed and Obtain interaction information with the peer user and determine the matching general response information, as well as obtain the corresponding expression style of the local user; which corresponds to display and resulting the interest reply information] .
It would have been obvious to one of an ordinary skill in the art, having the teachings of Shaobo and Lina before the affective filing date of the claimed invention to modify, Shaobo’s recommendation generate method by incorporating Lina’s reply specific style.
One would have been motivated to make such a combination in order to improve personalization and efficiency for accurate recommendations with user content outputs in interactive systems.
Regarding claims 2 and 13, Lina discloses wherein the release party information comprises at least one piece of historical interactive content of a release party; and
the performing style recognition comprises: performing style recognition on the at least one piece of historical interactive content to obtain a historical style category set respectively corresponding to the at least one piece of historical interactive content and a style category probability of each historical style category in the historical style category set [see para. 0012, 0031; converting the general response information according to the expression style to obtain interesting response information includes: if the expression style conforms to the set expression style, performing sentiment analysis on the general response information; and adding corresponding sentiment information to the general response information5 based on the result of the sentiment analysis to obtain interesting response information]; and performing weighted averaging on the style category probability of each historical style category, to obtain the second style category set to which the release party information belongs [see para. 0015, 0071; the local user's expression style includes one, the general reply information includes multiple, and one general reply information corresponds to one interesting reply information; displaying the interesting reply information includes: displaying each interesting reply information according to its candidate score and the association relationship between the peer user and the local user can also be obtained from the current communication application, and the association relationship can be used as association information. Among them, the relationship between the user on the local end and the user on the other end can be determined based on the chat history between the user on the local end and the user on the other end, the user on the local end's notes to the user on the other end, groups; which corresponds to determine combined expression styles from associated historical information].
Regarding claims 3 and 14, Lina discloses wherein the method further comprises: obtaining similar interactive content of the to-be-replied interactive content from interactive content of the media content; and the performing reply word prediction comprises [see para. 0012; the expression style to obtain interesting response information includes: performing word segmentation on the general response information to determine the corresponding general keywords; finding a second mapping relationship based on the expression style and the general keywords to determine the corresponding interesting keywords, wherein the second mapping information includes the relationship between the expression style, the general keywords, and the interesting keywords; and replacing the general keywords in the general response information with the corresponding interesting keywords to obtain interesting response information]:
performing reply word prediction based on the description content, the to-be-replied interactive content, the similar interactive content, and the style category vector to generate the reply content corresponding to each style category [see para. 0078; Replace the general keywords in the general reply information with the corresponding interesting keywords to obtain interesting reply information. For example, the common reply message "Just finished eating" might be replaced by a user whose expression style is "cute and sweet; which corresponds to predict interesting reply words by converting general replies on interactive content and description].
Regarding claims 4 and 15, Lina discloses wherein obtaining the similar interactive content comprises: separately obtaining a style similarity between the to-be-replied interactive content and each interactive content of the media content, and separately obtaining content similarity between the to-be-replied interactive content and each interactive content [see para. 0076; Perform word segmentation on the general response information to determine the corresponding general keywords]; and
selecting the similar interactive content of the to-be-replied interactive content from the interactive content of the media content based on the style similarity and the content similarity that correspond to each interactive content [After determining the general response information, natural language processing can be performed on the general response information, such as word segmentation, to obtain corresponding word segments. Then, the keywords of the general response information are determined based on the word segments. Then, based on the expression style of the user on this end and the general keywords, a second mapping relationship is found to determine the corresponding interesting keywords. The keywords in the general response information are replaced with interesting keywords to obtain interesting response information; which corresponds to search a mapping relation based on the expression style and general keywords].
Regarding claims 5 and 16, Shaobo discloses wherein the performing reply word prediction based on the description content, the to-be-replied interactive content, the similar interactive content, and the style category vector comprises: performing word segmentation on the description content, the to-be-replied interactive content, and the similar interactive content to obtain a segmented word set; performing vectorization processing on each word in the segmented word set to obtain a vectorized representation of each word in the segmented word set [see para. 0032, 0037; the categories of the object to be recommended may first be encoded to obtain a first encoding vector; then, among the plurality of preset categories, the preset category corresponding to the second encoding vector that is closest to the obtained first encoding vector is determined as the matching preset category, wherein each of the plurality of preset categories is pre-encoded as a second encoding vector and encoded in the same way as the category of the object to be recommended; which corresponds to recognize secondary categories styles and determining categories vector based analysis]; performing reply word prediction based on the vectorized representation of each word in the segmented word set and the style category vector, to obtain at least one predicted reply word corresponding to each style category; and separately generating the reply content corresponding to each style category based on the at least one predicted reply word corresponding to each style category [see para 0046; the obtained recommendation information template includes a text template with named entity wildcards. In this case, the target attribute words corresponding to the named entity wildcards of the object to be recommended obtained first, and then the target attribute words used to replace the corresponding named entity wildcards in the text template to generate recommendation information for the object to be recommended. The target attribute words of the object to be recommended refer to the descriptive words corresponding to the attributes that the object to be recommended possesses; which corresponds to generate words for recommendation outputs by filling template].
Regarding claims 6 and 17, Shaobo discloses wherein the performing reply word prediction based on the vectorized representation of each word in the segmented word set and the style category vector comprises: performing, targeting each style category, reply word prediction based on the vectorized representation of each word in the segmented word set and a style category vector of the targeted style category, to obtain a current reply word corresponding to the targeted style category [see para. 0007; determining the preset category that matches the category of the object to be recommended from a plurality of preset categories includes: encoding the category of the object to be recommended to obtain a first encoding vector; and determining the preset category corresponding to the second encoding vector that is closest to the obtained first encoding vector among the plurality of preset categories as the matching preset category, wherein each preset category among the plurality of preset categories is pre-encoded as a second encoding vector and encoded in the same way as the category of the object to be recommended]; using, based on the reply word prediction meeting a prediction stop condition, the current reply word as the at least one predicted reply word corresponding to the targeted style category [see para. 0079; Specifically, when the selected recommendation information template is a text template with named entity wildcards, the target attribute words (e.g., specific brand names and product names) corresponding to the named entity wildcards of the object to be recommended can be obtained; and the corresponding named entity wildcards in the text template replaced with the target attribute words to generate recommendation information for the object to be recommended]; and continuing to perform reply word prediction based on the vectorized representation of each word in the segmented word set, the style category vector of the targeted style category, and the current reply word based on the reply word prediction meeting a prediction continuing condition, to obtain the at least one predicted reply word corresponding to the targeted style category [see para. 0080; In scenarios where the object to be recommended is presented in the form of a video, multiple recommendation information templates selected from the recommendation information template group, which may include text templates with named entity wildcards as well as recommendation words. Then, the recommended objects are presented in video format. As the video plays, multiple recommended information items presented randomly or sequentially at pre-set video playback time points].
Regarding claims 12 and 20, Shaobo discloses a reply content processing apparatus, comprising: at least one memory configured to store program code; and at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising [see para. 0002, 0019; a computing device is provided, including a processor; and a memory configured to store computer executable instructions thereon, which, when executed by the processor, perform and the generation of recommendation information for the target audience (e.g., content, services, products, etc. provided by merchants or business providers) will inevitably be involved. The recommended information may include, for example, advertisements, promotional slogans, video comments and obtaining the category of an object to be recommended; determining a preset category from a plurality of preset categories that matches the category of the object to be recommended, each of the plurality of preset categories having at least one corresponding recommendation information template; obtaining a recommendation information template from the at least one recommendation information template corresponding to the matching preset category ; which corresponds to media content and user generated interactive data for reply recommendation];
first style recognition code configured to cause at least one of the at least one processor to perform encoding processing on description content of the media content and the to-be- replied interactive content to obtain a vectorized representation of each word in the description content and the to-be-replied interactive content [see para. 0040; The BERT model is based on the Transformer, which is the mainstream feature extractor in the field of Neuro-Linguistic Programming (NLP). Of course, any other suitable vectorization model or method can be used to encode the categories of the objects to be recommended, such as the Word2vec model, etc], and the to-be-replied interactive content to obtain a first style category set to which the to-be-replied interactive content belongs [see para. 0058; the various word segmentation techniques can be used to segment text information that meets preset text conditions. For example, when the text information that meets the preset text conditions is in English, word segmentation can be completed simply by using spaces and punctuation marks. When the text information that meets the preset text conditions is in Chinese, the Chinese text can be segmented using matching and statistical methods; which corresponds to recognize categories styles and determining categories based on vector];
second style recognition code configured to cause at least one of the at least one processor to perform style recognition based on release party information of the to-be-replied interactive content to obtain a second style category set to which the release party information belongs [see para. 0032, 0037; the categories of the object to be recommended may first be encoded to obtain a first encoding vector; then, among the plurality of preset categories, the preset category corresponding to the second encoding vector that is closest to the obtained first encoding vector is determined as the matching preset category, wherein each of the plurality of preset categories is pre-encoded as a second encoding vector and encoded in the same way as the category of the object to be recommended; which corresponds to recognize secondary categories styles and determining categories vector based analysis];
style distribution determining code configured to cause at least one of the at least one processor determine, according to the first style category set and the second style category set, a third style category set to which the to-be-replied interactive content belongs [see para. 0082, 0056; when the selected recommendation information template is a text template with named entity wildcards, the target attribute words (e.g., specific brand names and product names) corresponding to the named entity wildcards of the object to be recommended can be obtained; and the corresponding named entity wildcards in the text template can be replaced with the target attribute words to generate recommendation information for the object to be recommended; which corresponds to form a target category set];
style distribution determining code configured to cause at least one of the at least one processor determine a style category vector corresponding to each style category in the third style category set [see para. 0038, 0040; the categories of the object to be recommended may first be encoded to obtain a first encoding vector; then, among the plurality of preset categories, the preset category corresponding to the second encoding vector that is closest to the obtained first encoding vector is determined as the matching preset category, wherein each of the plurality of preset categories is pre-encoded as a second encoding vector and encoded in the same way as the category of the object to be recommended. The first and second encoding vectors can typically be, for example, embedding vectors encoded using word embedding techniques "; which corresponds to compute vectors for each category]; and
reply content generation code configured to cause at least one of the at least one processor to perform reply word prediction based on the description content, the to-be-replied interactive content, and the style category vector to generate reply content corresponding to each style category [see para 0046; the obtained recommendation information template includes a text template with named entity wildcards. In this case, the target attribute words corresponding to the named entity wildcards of the object to be recommended obtained first, and then the target attribute words used to replace the corresponding named entity wildcards in the text template to generate recommendation information for the object to be recommended. The target attribute words of the object to be recommended refer to the descriptive words corresponding to the attributes that the object to be recommended possesses; which corresponds to generate words for recommendation outputs by filling template]; however, Shaobao fails to explicitly teach interactive content obtaining code configured to cause at least one of the at least one processor to obtain to-be-replied interactive content for media content.
Lina discloses interactive content obtaining code configured to cause at least one of the at least one processor to obtain to-be-replied interactive content for media content [see para. 0038, interaction information with the peer user can be obtained to determine matching general reply information, and the expression style corresponding to the local user can be obtained. Then, the general reply information is converted according to the expression style to obtain interesting reply information, which is then displayed and Obtain interaction information with the peer user and determine the matching general response information, as well as obtain the corresponding expression style of the local user; which corresponds to display and resulting the interest reply information] .
It would have been obvious to one of an ordinary skill in the art, having the teachings of Shaobo and Lina before the affective filing date of the claimed invention to modify, Shaobo’s recommendation generate method by incorporating Lina’s reply specific style.
One would have been motivated to make such a combination in order to improve personalization and efficiency for accurate recommendations with user content outputs in interactive systems.
Regarding claims 13-17, directly or indirectly dependent on claim 12, essentially correspond to those of claims 2-6 respectively. Accordingly, the same reasoning as in claims 2-6 applies to claims 13-17.
Regarding claim 20 is an independent claim and relates to a non-transitory computer-readable storage medium storing computer code which, when executed by at least one processor, causes the at least one processor to at least. Since the features of claim 20 are substantially the same as those of claim 12 except for the category of invention, the same reasoning as in claim 12 applies to claim 20.
Allowable Subject Matter
Claims 7-11 and 18-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892).
Wang et al. (US2022/0113793) discloses acquiring a current application scene of a user; acquiring a target object type in conversation with the user; generating at least one candidate reply content corresponding to the application scene and matching with the target object type in an expression style; and adjusting the expression style of the candidate reply content according to an expression style of the user on historical content, to generate at least one target reply content.
DUAN et al. (US2022/0027577) discloses a first natural language is received; the first natural language text is converted, via a text generation model, into a second natural language text that at least partly reflects the meaning of the first natural language text and has a style distinguishable from the first natural language text, the text generation model comprising a modifiable parameter; and in response to receiving a modification to the parameter, the first natural language text is converted, via the text generation model, into a third natural language text that at least partly reflects the meaning of the first natural language text and includes a style distinguishable from both the first natural language text and the second natural language text.
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for all that it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed were instead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck & Co. v. Biocraft Labs., Inc., 874 F.2d 804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1,215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163 USPQ 545, 549 (CCPA 1969).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAO H NGUYEN whose telephone number is (571)272-4053. The examiner can normally be reached on Mon-Fri 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kieu Vu can be reached on 571-272-4057. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CAO H NGUYEN/ Primary Examiner, Art Unit 2171