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 Objections
Claims 7 and 18 objected to because of the following informalities:
Claim 7 recites “wherein the type of object comprises one of a container object and a contained object.” However, there is no antecedent basis for “the type of object” in Claim 7, 5, or 1. The type of object is first defined in claim 6.
Claim 18 similarly recites “wherein the type of object comprises one of a container object and a contained object.” However, there is no antecedent basis for “the type of object” in Claim 18, 16, or 12. The type of object is first defined in claim 17.
Appropriate correction is required.
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, 3, 4, 8, 9, 10, 11, 12, 13, 14, 15, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sensui (US 20220080309 A1) in view of Seuntiens (US 20150278896 A1).
Regarding claim 1:
Sensui teaches:
A method for generating an ambience suggestion for an environment (Sensui: In the case where the user selects receiving advice, a process of arranging the “recommended furniture article” is performed [0107]), the method comprising:
processing one or more image frames corresponding to the environment (Sensui: FIG. 2 illustrates an image obtained by photographing the room 201, which is presented as a “question stage”, from above with a virtual camera [0072]);
determining a fitness score corresponding to each of the one or more objects based on one or more parameters (Sensui: In the exemplary embodiment, a score is set for each furniture article such that a higher score is acquired with a furniture article suited to the “suited theme”. [0078]);
determining a first ambience score based on the determined fitness scores corresponding to the one or more objects (Sensui: a user score is calculated by summing the scores that are set for furniture articles arranged in the respective arrangement areas 203 [0090]);
identifying a target space in the environment (Sensui: the processor 121 selects an arrangement area 203 in which a furniture article is to be arranged, and selects a furniture article to be arranged [0172]);
identifying a target space in the environment based on the determined fitness scores corresponding to the one or more objects (Sensui: if furniture articles have been arranged in all the arrangement areas, a representation in which a furniture article having a lowest score among the already arranged furniture articles is replaced with the “recommended furniture article” corresponding to the arrangement area 203 is displayed. [0107]; see Note 1B);
generating an object arrangement for the target space based on the environment (Sensui: in step S66, the processor 121 performs a process of […] arranging the “recommended furniture article” in the selected arrangement area 203 [0173]);
determining a second ambience score of the environment based on the generated object arrangement (Sensui: in step S67, the processor 121 recalculates a user score on the basis of the state of the virtual space (room 201) after the “recommended furniture article” is arranged [0174]);
comparing the first ambience score and the second ambience score (Sensui: control may be performed on the basis of the user score at that time such that a higher score is acquired but only the closest pass line 212 is achieved [0110]; see Note 1A); and
recommending the ambience suggestion based on the generated object arrangement based on determining that the second ambience score is greater than the first ambience score, wherein the ambience suggestion indicates a change in the environment based on the generated object arrangement (Sensui: in step S70, the processor 121 displays a game image showing a representation of arranging the “recommended furniture article”, on the display section 124, [0175]).
Note 1A: In [0110], Sensui teaches that a higher score than the previous user score may be determined. In order to determine that the newly generated second ambience score is higher than the first, the system must compare the two scores.
Note 1B: In other words, Sensui chooses an arrangement area that contributes the least to the user score as a location to place a recommended furniture item.
Sensui fails to teach:
processing one or more image frames corresponding to the environment to identify one or more objects in the environment;
Seuntiens teaches:
processing one or more image frames corresponding to the environment (Seuntiens: In a first step 2010 an image of a scene is received, after which in a second step 2020 the image is analyzed in order to determine a scene related variable [0089]) to identify one or more objects in the environment (Seuntiens: Analyzing the image can comprise determining a position in the scene for placing a lighting device, for example through object recognition. [0090]);
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Seuntiens with Sensui. Processing one or more image frames corresponding to the environment to identify one or more objects in the environment, as in Seuntiens, would benefit the Sensui teachings by enabling the system to determine how best to place lights based on the objects already present: “lighting device designs can then be placed in a logical position based on object recognition.” [0090]
Regarding claim 2:
Sensui in view of Seuntiens teaches:
The method as claimed in claim 1 (as shown above), wherein the one or more parameters comprises at least one of
environment theme (Sensui: a score is set for each furniture article such that a higher score is acquired with a furniture article suited to the “suited theme”. For example, in the case where “country” is defined as the suited theme of a “desk A”, and “modern” is defined as the suited theme of a “desk B”, a higher score is acquired (or is highly likely to be acquired) when the “desk A” is arranged in the above-described room 201, which has a theme of “country”, than when the “desk B” is arranged in the room 201. [0078]),
user interest (Seuntiens: Selections made by the user are used to make further suggestions [0063]; see Note 2A),
object location (Seuntiens: Preferably, the selection of a lighting device design type is limited to those lighting device design types that are compatible with the scene selected. As an example, selecting a bathroom as the scene limits the lighting device design types that are selectable to those that are safe to use in a bathroom [0043]; see Note 2A),
and object usage (Seuntiens: the suggestion is based on the task a user performs at that position in the scene (i.e. the user might appreciate cool white light for reading documents while seated at the desk). [0045]; see Note 2A and Note 2B).
Note 2A: Claim 1 of the present application recites: “determining a fitness score corresponding to each of the one or more objects based on one or more parameters”. Claim 1 further recites that the fitness score is used to generate an ambience score which is used to generate ambience suggestions for a user. Seuntiens teaches that various factors, such as selections made by the user, the selected scene, and how long the scene has been viewed with that particular object may affect suggestions. Therefore, the Examiner submits that it would be obvious for one of ordinary skill in the art to include these factors in the score generated by Sensui.
Note 2B: The Examiner interpreted the discussion of tasks performed at a location in a scene at [0045] in Seuntiens to be analogous to “object usage”, because the specification of the present application teaches: “Uusage may correspond to a value which defines usability of the object in the environment. ” (Pg. 12).
Regarding claim 3:
Sensui in view of Seuntiens teaches:
The method as claimed in claim 1 (as shown above), wherein the first ambience score comprises a sum of the fitness scores corresponding to the one or more objects (Sensui: the information processing program may cause the computer to: calculate the score by summing sub scores calculated respectively for arrangement locations at which the game objects are to be arranged; [0013]).
Regarding claim 4:
Sensui in view of Seuntiens teaches:
The method as claimed in claim 1 (as shown above), wherein the object arrangement includes a re-arrangement of at least one of the one or more identified objects, a replacement of the at least one of the one or more identified objects (Sensui: a process of replacing an arranged furniture article with the “recommended furniture article”, or a process of newly arranging the “recommended furniture article” in the arrangement area 203 in which no furniture article has been arranged, is performed [0107]), or an addition of a new object at the target space (Sensui: (“arrangement” of a furniture article in the following description means both new arrangement and replacement) [0107]).
Regarding claim 8:
Sensui in view of Seuntiens teaches:
The method as claimed in claim 1 (as shown above), comprising:
monitoring one or more user activities in the environment (Seuntiens: The profile can also include information on how long a user has viewed a scene with a first lighting device design [0045]);
determining a user interest based on the one or more user activities (Seuntiens: Suggestions can also be based on selections the user has made […] The various selections a user makes can be used to create a user profile. [0045]); and
generating the object arrangement for the target space based on the determined user interest (Sensui: a process of replacing an arranged furniture article with the “recommended furniture article”, or a process of newly arranging the “recommended furniture article” in the arrangement area 203 in which no furniture article has been arranged, is performed [0107]; see Note 8A).
Note 8A: In [0045], Seuntiens teaches that suggestions of furniture or lighting may be determined based on user interest (e.g., how long an item has been viewed in a scene). Sensui teaches that a recommended furniture item may be used in generating a furniture arrangement: “a process of replacing an arranged furniture article with the “recommended furniture article”, or a process of newly arranging the “recommended furniture article” in the arrangement area 203 in which no furniture article has been arranged, is performed” [0107]. When the teachings of Seuntiens are combined with Sensui, it would be obvious to one of ordinary skill in the art to select a furniture item based on user interest and utilize the furniture item in a furniture arrangement.
Regarding claim 9:
Sensui in view of Seuntiens teaches:
The method as claimed in claim 1 (as shown above), comprising:
determining one or more user-related events (Sensui: the case where the user selects receiving advice in the advice confirmation dialog 242 in FIG. 11 described above [0107]; see Note 9A); and
generating the object arrangement for the target space based on the one or more user-related events (Sensui: In the case where the user selects receiving advice, a process of arranging the “recommended furniture article” is performed in this example [0107]).
Note 9A: The Examiner interpreted user-related events broadly, as the term only appears twice in the specification and does not seem to refer to a specific kind of user event.
Regarding claim 10:
Sensui in view of Seuntiens teaches:
The method as claimed in claim 1 (as shown above), comprising:
determining one or more additional characteristics of environment, the one or more additional characteristics comprising color of the identified objects (Sensui: For example, if the arranged furniture articles all have the same color, bonus points may be further added. [0090]); and
generating the object arrangement for the target space based on the one or more additional characteristics of environment (see Note 10A).
Note 10A: Sensui teaches that: “in the “advice function”, in the case where the user score is a little short of any pass line 212 as described above, the furniture arrangement state is changed such that the user score becomes at least equal to or higher than the next pass line 212” [0108]. That is, the object arrangement for the target space is regenerated in order to increase the score. Sensui further teaches: “In another exemplary embodiment, a user score may be calculated by further combining another element in addition to the scores for the arranged furniture articles. For example, if the arranged furniture articles all have the same color, bonus points may be further added” [0090]. In other words, an object arrangement may be generated based on the color of all furniture articles being the same in order to increase the score.
Sensui fails to teach:
determining one or more additional characteristics of environment, the one or more additional characteristics comprising material of the identified objects, lighting condition of the environment, and/or space occupancy in the environment;
Seuntiens teaches:
determining one or more additional characteristics of environment, the one or more additional characteristics comprising
color of the identified objects, material of the identified objects (Seuntiens: analysis of the model of the lighting device design can determine, for example, the size, shape and potentially also color and materials of the lighting device design […] Each of these analyses can determine a lighting device design related variable, such as the type of lighting device design (e.g. ceiling lamp) or the color of the lamp hood (e.g. red). [0080]; see Note 10B),
lighting condition of the environment, (Seuntiens: image analysis of the scene comprising the lighting device design can determine the same variables as the image analysis of the lighting device design [0080]; see Note 10C)
and/or space occupancy of the environment (Seuntiens: Similarly an open area can be detected where a standing lamp could fit, or in a more advanced application of the method suggestions can be made for the user to reposition furniture, a plant or existing lamps [0090]);
Note 10B: In [0080] cited above, Seuntiens teaches that analysis of a model of lighting device design can determine a color or material, and that the analysis can determine corresponding object to the variable (i.e., the color of a lamp hood in the scene is determined).
Note 10C: Seuntiens teaches: “As a second example, image analysis of the image of the lighting device design can determine shape, color, materials and potentially also lighting properties (e.g. brightness, light distribution) of the lighting device design. As a third example, image analysis of the scene comprising the lighting device design can determine the same variables as the image analysis of the lighting device design” [0080], emphasis added. That is, Seuntiens teaches that the brightness and light distribution of the scene may be determined.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Seuntiens with Sensui. Determining one or more additional characteristics of environment, as in Seuntiens, would benefit the Sensui teachings by enabling more tailored and complex analysis: “Multiple analyses can be combined to determine, for example, more complex variables such as style (e.g. classical, modern) or enhance the analysis capabilities,” (Seuntiens, [0080]).
Regarding claim 11:
Sensui teaches:
The method as claimed in claim 1 (as shown above), comprising:
generating a virtual environment corresponding to the environment (Sensui: FIG. 2 illustrates an image obtained by photographing the room 201, which is presented as a “question stage”, from above with a virtual camera [0072]; Sensui: Specifically, the processor 121 refers to the question stage data 503, constructs a virtual space corresponding to the question stage [0149]); and
rendering the recommended ambience suggestion in the virtual environment (Sensui: In FIG. 12, a furniture article object 208 that is a “chair” and is also a “recommended furniture article” is arranged in the arrangement area 203C in which no furniture article has been arranged before the advice. [0107]; see Note 11A).
Note 11A: Fig. 12 showcases a user interface including the virtual environment. Sensui teaches that the recommended furniture article is included in the virtual environment at 203C.
Regarding claim 12:
Claim 12 is substantially similar to claim 1, and is therefore rejected for similar reasons. Claim 12 contains the following notable differences:
Claim 12 claims a system instead of a method. Sensui teaches a system: “A system configured to generate an ambience suggestion for an environment, the system comprising: a memory (Sensui: memory 122 [0066]); at least one processor (Sensui: the information processing terminal 102 includes a processor 121 [0066]), comprising processing circuitry, operably coupled to the memory, wherein at least one processor, individually and/or collectively (Sensui: The processor 121 may include a single processor or a plurality of processors. In the memory 122, various programs to be executed by the processor 121 and various kinds of data to be used in the programs are stored. [0066]), is configured to:”
Regarding claim 13:
Claim 13 is substantially similar to claim 2, and is therefore rejected for similar reasons. Claim 13 contains the following notable differences:
Claim 13 claims a system instead of a method. In the rejection of claim 12, it was shown that Sensui teaches a system.
Regarding claim 14:
Claim 14 is substantially similar to claim 3, and is therefore rejected for similar reasons. Claim 14 contains the following notable differences:
Claim 14 claims a system instead of a method. In the rejection of claim 12, it was shown that Sensui teaches a system.
Regarding claim 15:
Claim 15 is substantially similar to claim 4, and is therefore rejected for similar reasons. Claim 15 contains the following notable differences:
Claim 15 claims a system instead of a method. In the rejection of claim 12, it was shown that Sensui teaches a system.
Regarding claim 19:
Claim 19 is substantially similar to claim 8, and is therefore rejected for similar reasons. Claim 19 contains the following notable differences:
Claim 19 claims a system instead of a method. In the rejection of claim 12, it was shown that Sensui teaches a system.
Regarding claim 20:
Claim 20 is substantially similar to claim 9, and is therefore rejected for similar reasons. Claim 20 contains the following notable differences:
Claim 20 claims a system instead of a method. In the rejection of claim 12, it was shown that Sensui teaches a system.
Claims 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sensui (US 20220080309 A1) in view of Seuntiens (US 20150278896 A1) and Ghadar (US 20190295151 A1).
Regarding claim 5:
Sensui in view of Seuntiens teaches:
The method as claimed in claim 1 (as shown above), comprising:
Sensui in view of Seuntiens fails to teach:
determining an object threshold value corresponding to each of the one or more identified objects;
comparing the fitness score of each object with a corresponding object threshold value; and
identifying the target space in the environment based on the comparison of the first score of each object with the corresponding object threshold value.
Ghadar teaches:
determining an object threshold value corresponding to each of the one or more identified objects;
comparing the fitness score of each object with a corresponding object threshold value (Ghadar: A recommended visually compatible item may be an item whose match score is higher than a threshold score or ranked within a certain number of positions [0037]); and
identifying the target space in the environment (Ghadar: various embodiments provide for recommending products from an electronic catalog that are aesthetically compatible with an existing items in a physical space [0015]) based on the comparison of the first score of each object with the corresponding object threshold value (Ghadar: A recommended visually compatible item may be an item whose match score is higher than a threshold score or ranked within a certain number of positions [0037]).
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Ghadar with Sensui in view of Seuntiens. Identifying the target space in the environment based on the comparison of the first score of each object with the corresponding object threshold value, as in Ghadar, would benefit the Sensui in view of Seuntiens teachings by ensuring a that an object is chosen that matches the scene to at least a minimal degree.
Regarding claim 16:
Claim 16 is substantially similar to claim 5, and is therefore rejected for similar reasons. Claim 16 contains the following notable differences:
Claim 16 claims a system instead of a method. In the rejection of claim 12, it was shown that Sensui teaches a system.
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Sensui (US 20220080309 A1) in view of Seuntiens (US 20150278896 A1), and Yu (NPL: Make it Home: Automatic Optimization of Furniture Arrangement).
Regarding claim 6:
Sensui in view of Seuntiens teaches:
The method as claimed in claim 1 (as shown above), comprising:
determining a type of object for each of the identified one or more objects (Sensui: The furniture article type information 522 is information for indicating the “type” of the furniture article. [0126]);
determining an orientation and a position of each of the identified one or more objects (Sensui: Thus, in the game, a furniture article can be arranged, through a single tapping operation, including the orientation of the furniture article. [0079]);
Sensui in view of Seuntiens fails to explicitly teach:
determining one or more neighboring objects corresponding to each of the one or more identified objects based on the orientation and the position corresponding to the identified object;
generating one or more clusters of objects based on the determined one or more neighboring objects and the corresponding identified object; and
generating the object arrangement for the target space based at least on the type of object corresponding to the identified one or more objects at the target space and the generated one or more clusters of objects for the corresponding identified one or more objects at the target space.
Yu teaches:
determining a type of object for each of the identified one or more objects (Yu: The optimization formulation can be readily extended to second tier objects—optimization is performed to move second-tier objects on the supporting surfaces provided by their first-tier counterparts in the same way that furniture objects move over the floor space of a room, which is regarded as the root in the hierarchy. Pg. 7, par. 3; see Note 6A);
determining an orientation and a position of each of the identified one or more objects (Yu: The basic move of the optimization modifies the position of an object and its orientation, Pg. 5, Section 4.2: Proposed Moves, Translation and Rotation);
determining one or more neighboring objects corresponding to each of the one or more identified objects based on the orientation and the position corresponding to the identified object (Yu: key prior relationships are the distance of an object to its nearest wall di and its relative orientation to the wall θi, Pg. 4, Section 3.2: Learning Prior Relationships);
generating one or more clusters of objects based on the determined one or more neighboring objects and the corresponding identified object (Yu: They are respectively estimated as the clustered means of input examples, where we can assign one of the clustered means as di and θi respectively for object i during optimization. The number of clusters can be preset or estimated by [Grunwald 2007], Pg. 4, Section 3.2: Learning Prior Relationships, Spatial relationships); and
generating the object arrangement for the target space based at least on the type of object corresponding to the identified one or more objects at the target space (Yu: Given two objects A and B, object A is defined as the parent of B (and B as the child of A) if A is supporting B by a certain surface, Pg. 4, Section 3.2: Learning Prior Relationships, Hierarchical relationships) and the generated one or more clusters of objects for the corresponding identified one or more objects at the target space (Yu: Given the spatial relationships extracted as described above, our goal is to integrate this information into an optimization framework […] Given an arbitrary room layout populated by furniture objects, the synthesized arrangement should be useful for virtual environment modeling in games and movies, interior design software, and other applications, Pg. 4, Section 4: Furniture Arrangement Optimization, par. 1; see Note 6B).
Note 6A: Yu teaches first and second tier objects in par. 3 on Pg. 7 as cited above, as well as on Pg. 4, Section 3.2, par. 3: “All objects supported by a surface of a first-tier object (e.g., a vase on top of a cupboard) are defined as “second-tier objects”. A room configuration is thus represented by a hierarchy of relationships.” The Examiner submits that the hierarchy taught by Yu distinguishes objects by a container object (in Yu’s example, the cupboard) and the contained object (in Yu’s example, the vase).
Note 6B: Yu teaches that a furniture arrangement may be synthesized based on the “spatial relationships extracted as described above”. The section “above” (immediately prior to section 4) is Section 3.2: Learning Prior Relationships, which describes the one or more clusters of objects and type of objects as cited above.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Yu with Sensui in view of Seuntiens. Generating the object arrangement for the target space based at least on the type of object corresponding to the identified one or more objects at the target space and the generated one or more clusters of objects for the corresponding identified one or more objects at the target space, as in Yu, would benefit the Sensui in view of Seuntiens teachings by enabling enhancements to be generated for arbitrary furniture layouts: “Given an arbitrary room layout populated by furniture objects, the synthesized arrangement should be useful for virtual environment modeling in games and movies, interior design software, and other applications.” (Pg. 4, Section 4: Furniture Arrangement Optimization, par. 1)
Regarding claim 17:
Claim 17 is substantially similar to claim 6, and is therefore rejected for similar reasons. Claim 17 contains the following notable differences:
Claim 17 claims a system instead of a method. In the rejection of claim 12, it was shown that Sensui teaches a system.
Claims 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sensui (US 20220080309 A1) in view of Seuntiens (US 20150278896 A1), Ghadar (US 20190295151 A1), and Yu (NPL: Make it Home: Automatic Optimization of Furniture Arrangement).
Regarding claim 7:
Sensui in view of Seuntiens and Ghadar teaches:
The method as claimed in claim 5 (as shown above),
Sensui in view of Seuntiens and Ghadar fails to teach:
wherein the type of object comprises one of a container object and a contained object.
Yu teaches:
wherein the type of object comprises one of a container object and a contained object (Yu: Given two objects A and B, object A is defined as the parent of B (and B as the child of A) if A is supporting B by a certain surface, Pg. 4, Section 3.2: Learning Prior Relationships, Hierarchical relationships; see also Note 6A and Note 7A).
Note 7A: The Examiner interpreted the “type of object” in claim 7 as the same “type of object” described in claim 6 due to an antecedent basis issue (see Objection above). A similar issue is present with claim 18 as well.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Yu with Sensui in view of Seuntiens and Ghadar. Generating the object arrangement for the target space based at least on the type of object corresponding to the identified one or more objects at the target space and the generated one or more clusters of objects for the corresponding identified one or more objects at the target space, as in Yu, would benefit the Sensui in view of Seuntiens and Ghadar teachings by enabling enhancements to be generated for arbitrary furniture layouts: “Given an arbitrary room layout populated by furniture objects, the synthesized arrangement should be useful for virtual environment modeling in games and movies, interior design software, and other applications.” (Pg. 4, Section 4: Furniture Arrangement Optimization, par. 1)
Regarding claim 18:
Claim 18 is substantially similar to claim 7, and is therefore rejected for similar reasons. Claim 18 contains the following notable differences:
Claim 18 claims a system instead of a method. In the rejection of claim 12, it was shown that Sensui teaches a system.
Conclusion
The Examiner identified potential limitation(s) in the specification that would overcome the prior art rejections under 103 if amended into the claims. Note that in such a situation, further search and consideration would be required:
Equation 1 (as recited on Pg. 10 of the specification of the present application) or Equation 2 (as cited on Pg. 13 of the specification of the present application).
“a numerical and/or logical value may be assigned to the LLocation based on determined value. For example, for an inaccessible object, the value of LLocation may be defined as zero, and for an accessible object, the value of LLocation may be defined as one” as on Pg. 12 of the specification of the present application.
“the threshold definer may be configured to define a threshold for monitoring the user before recommending the ambience suggestion(s). For example, the system 102 may define the threshold as five days” as on Pg. 29 of the specification of the present application.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT ALEXANDER PROVIDENCE whose telephone number is (571)270-5765. The examiner can normally be reached Monday-Thursday 8:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, King Poon can be reached at (571)270-0728. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VINCENT ALEXANDER PROVIDENCE/Examiner, Art Unit 2617 /KING Y POON/Supervisory Patent Examiner, Art Unit 2617
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